# Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation

Shyam Nandan Rai, Fabio Cermelli, Barbara Caputo, and Carlo Masone S. N. Rai,
F. Cermelli, B. Caputo, and C. Masone are with Politecnico di Torino. E-mail:
<EMAIL_ADDRESS>
###### Abstract

Segmenting unknown or anomalous object instances is a critical task in
autonomous driving applications, and it is approached traditionally as a per-
pixel classification problem. However, reasoning individually about each pixel
without considering their contextual semantics results in high uncertainty
around the objects’ boundaries and numerous false positives. We propose a
paradigm change by shifting from a per-pixel classification to a mask
classification. Our mask-based method, Mask2Anomaly, demonstrates the
feasibility of integrating a mask-classification architecture to jointly
address anomaly segmentation, open-set semantic segmentation, and open-set
panoptic segmentation. Mask2Anomaly includes several technical novelties that
are designed to improve the detection of anomalies/unknown objects: i) a
global masked attention module to focus individually on the foreground and
background regions; ii) a mask contrastive learning that maximizes the margin
between an anomaly and known classes; iii) a mask refinement solution to
reduce false positives; and iv) a novel approach to mine unknown instances
based on the mask- architecture properties. By comprehensive qualitative and
qualitative evaluation, we show Mask2Anomaly achieves new state-of-the-art
results across the benchmarks of anomaly segmentation, open-set semantic
segmentation, and open-set panoptic segmentation.

###### Index Terms:

Anomaly Segmentation, Open-set Semantic Segmentation, Open-set Panoptic
Segmentation, Mask Transformers.

##  1 Introduction

Image segmentation [15, 51, 60, 58, 53] plays a significant role in self-
driving cars, being instrumental in achieving a detailed understanding of the
vehicle’s surroundings. Generally, segmentation models are trained to
recognize a pre-defined set of semantic classes (e.g., car, pedestrian, road,
etc.); however, in real-world applications, they may encounter objects not
belonging to such categories (e.g., animals or cargo dropped on the road).
Therefore, it is essential for these models to identify objects in a scene
that are not present during training i.e., anomalies, both to avoid potential
dangers and to enable continual learning [44, 9, 19, 8] and open-world
solutions [7]. The segmentation of unseen object categories can be performed
at three levels of increasing semantic output information (see Fig. 1):

  * •

_Anomaly segmentation_ (AS) [5, 57, 22, 33] focuses on segmenting objects from
classes that were absent during training, generating an output map that
identifies the anomalous image pixels.

  * •

_Open-set semantic segmentation_ (OSS) [26] evaluates a segmentation model’s
performance on both anomalies and known classes. OSS ensures that when
training an anomaly segmentation model, its performance on known classes
remains unaffected.

  * •

_Open-set panoptic segmentation_ (OPS) [32] simultaneously segments distinct
instances of unknown objects and performs panoptic segmentation [34] for the
known classes.

In the literature, AS, OSS and OPS are typically addressed separately using
specialized networks for each task. These networks rely on per-pixel
classification architectures that individually classify the pixels and assign
to each of them an anomaly score. However, reasoning on the pixels
individually without any spatial correlation produces noisy anomaly scores,
thus leading to a high number of false positives and poorly localized
anomalies or unknown objects (see Fig. 2).

Figure 1: Mask2Anomaly: We present a mask-based architecture that can jointly
perform open-set semantic segmentation, open-set panoptic segmentation, and
anomaly segmentation. In the figure, the objects enclosed in red boxes are
anomaly/unknown.

In this paper, we propose to jointly address AS, OSS, and OPS with a single
architecture (with minor changes during inference) by casting them as a mask
classification task rather than a pixel classification task (see Fig. 1). The
idea of employing mask-based architecture stems from the recent advances in
mask-transformer architectures [13, 14], which demonstrated that it is
possible to achieve remarkable performance across various segmentation tasks
by classifying masks rather than pixels. We hypothesize that mask-transformer
architectures are better suited to detect anomalies than per-pixel
architectures because masks encourage objectness and thus can capture
anomalies as whole entities, leading to more congruent anomaly scores and
reduced false positives. However, the effectiveness of mask-transformer
architectures hinges on the capability to output masks that captures anomalies
well. Hence, we propose several technical contributions to improve the
capability of mask-transformer architectures to capture anomalies or unknown
objects and minimize false positives:

  * •

At the architectural level, we propose a global masked-attention mechanism
that allows the model to focus on both the foreground objects and on the
background while retaining the efficiency of the original masked-attention
[13].

  * •

At the training level, we have developed a mask contrastive learning framework
that utilizes outlier masks from additional out-of-distribution data to
maximize the separation between anomalies and known classes.

  * •

At the inference level, for anomaly segmentation, we propose a mask-based
refinement solution that reduces false positives by filtering masks based on
the panoptic segmentation that distinguishes between “things” and “stuff” and
for open-set panoptic segmentation, we developed an approach to mine unknown
instances based on mask-architecture properties.

Figure 2: Per-pixel vs per-mask architecture: We show significant shortcoming
in the performance of state-of-the-art methods employing per-pixel
architectures for anomaly segmentation or open-set segmentation tasks. These
methods prediction have significant false positives and noisy outcomes.
Mask2Anomaly(ours), an architecture based on mask-transformer properties that
effectively addresses both anomaly segmentation and open-set segmentation
tasks, leading to a substantial reduction in false positives and enhancing
overall prediction quality.

We integrate these contributions on top of the mask architecture [13] and term
this solution Mask2Anomaly. To the best of our knowledge, Mask2Anomaly is the
first universal architecture that jointly addresses AS, OSS, and OPS task and
segment anomalies or unknown objects at the mask level. We tested Mask2Anomaly
on standard anomaly segmentation benchmarks (Road Anomaly [40], Fishyscapes
[5], Segment Me If You Can [10], Lost&Found [48]), open-set semantic
segmentation benchmark (Streethazard [29]), and open-set panoptic MS-COCO [32]
dataset, achieving the best results among all methods for all task by a
significant margin. In particular, Mask2Anomaly reduces the false positives
rate by more than half on average and improves the open-set metric performance
by one-third w.r.t the previous state-of-the-art. Code and pre-trained models
will be made publicly available upon acceptance.

This work is an extension of our previous paper [49] that was accepted to ICCV
2023 (Oral) with the following contributions:

  * •

We extend Mask2Anomaly to open-set segmentation tasks, namely open-set
semantic segmentation and open-set panoptic segmentation.

  * •

For the open-set panoptic segmentation task, we developed a novel approach to
mine unknown instances based on the properties of the mask-architecture and
provide related ablation studies to show its efficacy.

  * •

Extensive qualitative and quantitative experiments demonstrate that
Mask2Anomaly is an effective approach to address open-set segmentation tasks.
Notably, Mask2Anomaly gives a significant gain of 30% on Open-IoU metrics
w.r.t best existing method.

  * •

We extend Mask2Anomaly experimentation for the anomaly segmentation task by
showing results on the Lost&Found dataset. Also, we show global mask attention
can positively impact semantic segmentation by investigating its
generalizability to other datasets.

##  2 Related Work

Mask-based semantic segmentation. Traditionally, semantic segmentation methods
[42, 12, 62, 38, 61] have adopted fully-convolutional encoder-decoder
architectures [42, 1] and addressed the task as a dense classification
problem. However, transformer architectures have recently caused us to
question this paradigm due to their outstanding performance in closely related
tasks such as object detection [6] and instance segmentation [27]. In
particular, [14] proposed a mask-transformer architecture that addresses
segmentation as a mask classification problem. It adopts a transformer and a
per-pixel decoder on top of the feature extraction. The generated per-pixel
and mask embeddings are combined to produce the segmentation output. Building
upon [14], [13] introduced a new transformer decoder adopting a novel masked-
attention module and feeding the transformer decoder with one pixel-decoder
high-resolution feature at a time.

So far, all these mask-transformers have been considered exclusively in a
closed set setting, i.e, there are no unknown categories at test time. To the
best of our knowledge, Mask2Anomaly is the first method that performs AS
directly with mask-transformers, thus empowering these approaches with the
capability to recognize anomalies in real-world settings.

Anomaly segmentation methods can be broadly divided into three categories: (a)
Discriminative, (b) Generative, and (c) Uncertainty-based methods.
Discriminative Methods are based on the classification of the model outputs.
Hendrycks and Gimpel [30] established the initial AS discriminative baseline
by applying a threshold over the maximum softmax probability (MSP) that
distinguishes between in-distribution and out-of-distribution data. Other
approaches use auxiliary datasets to improve performance [37, 33, 54] by
calibrating the model over-confident outputs. Alternatively, [36] learns a
confidence score by using the Mahalanobis distance, and [11] introduces an
entropy-based classifier to discover out-of-distribution classes. Recently,
discriminative methods tailored for semantic segmentation [5] directly segment
anomalies in embedding space. Generative Methods  provides an alternative
paradigm to segment anomalies based on generative models [40, 17, 57, 56].
These approaches train generative networks to reconstruct anomaly-free
training data and then use the generation discrepancy to detect an anomaly at
test time. All the generative-based methods heavily rely on the generation
quality and thus experience performance degradation due to image artifacts
[22]. Finally, Uncertainty based methods segment anomalies by leveraging
uncertainty estimates via Bayesian neural networks [46].

Open-set segmentation is the task of segmenting both the the anomalies and in-
distribution classes for a given image. Anomaly segmentation methods [31, 57]
can be adapted to perform open-set semantic segmentation by fusing the in-
distribution segmentation results. However, these methods show poor
performance in open-set metrics because their in-distribution class
segmentation capabilities degrade after training for anomaly segmentation. [2]
formally introduces the problem of open-set semantic segmentation that uses
multi-task model segment anomaly and predicts semantic segmentation maps.
Later, [3] improved the prior method using noisy outlier labels. Recently,
[26] proposed a hybrid approach that combines the known class posterior,
dataset posterior, and an un-normalized data likelihood to estimate anomalies
and in-distribution classes simultaneously. Another challenging problem in the
space of open-set segmentation is open-set panoptic segmentation [32]. In
open-set panoptic segmentation, the goal is to simultaneously segment distinct
instances of unknown objects and perform panoptic segmentation for in-
distribution classes. Hwang et.al. [32] proposed an exemplar-based open-set
panoptic segmentation network (EOPSN) that is based on exemplar theory and
utilizes Panoptic FPN [34] which is a per-pixel architecture to perform open-
set panoptic segmentation.

All the methods discussed so far for anomaly and open-set segmentation rely on
per-pixel classification and evaluate individual pixels without considering
local semantics. This approach often leads to noisy anomaly predictions,
resulting in significant false positives and reduced in-distribution class
segmentation performance. Mask2Anomaly overcomes this limitation by segmenting
anomalies and in-distribution classes as semantically clustered masks,
encouraging the objectness of the predictions. To the best of our knowledge,
this is the first work to use masks both to segment anomalies and for open-set
segmentation.

##  3 Preliminaries

Notations: Let us denote $\mathcal{X}\subset\mathbb{R}^{3\times H\times W}$
the space of RGB images, where $H$ and $W$ are the height and width,
respectively, and with $\mathcal{Y}\subset\mathbb{N}^{Z\times H\times W}$ the
space of semantic labels that associate each pixel in an image to a semantic
category from a predefined set $\mathcal{Z}$, with $|\mathcal{Z}|=Z$. At
training time we assume to have a dataset
$\mathcal{D}=\left\\{(x_{i},y_{i})\right\\}_{i=1}^{D}$, where
$x_{i}\in\mathcal{X}$ is an image and $y_{i}\in\mathcal{Y}$ is its ground
truth having pixel-wise semantic class labels. Alternatively, $\mathcal{Y}$
can also be described as the semantic partition of the image into $Z$ regions
that are represented as a set of binary masks $M^{gt}$, where the ground-truth
labels of $x_{i}$ can be represented as
$M^{gt}=\\{m_{i}|m_{i}\in[0,1]^{H\times W}\\}_{i=1}^{Z}$.

Mask architectures: The prototypical mask architecture consists of three meta
parts: a) a backbone that acts as feature extractor, b) a pixel-decoder that
upsamples the low-resolution features extracted from the backbone to produce
high-resolution per-pixel embeddings, and c) a transformer decoder, made of
$L$ transformer layers, that takes the image features to output a fixed number
of object queries consisting of mask embeddings and their associated class
scores $C\in\mathbb{R}^{N\times Z}$. The final class masks
$M\in\mathbb{R}^{N\times(H\times W)}$ are obtained by multiplying the mask
embeddings with the per-pixel embeddings obtained from the pixel-decoder.

During training we use the Hungarian algorithm to match ground truth masks
$M^{gt}$ with the predicted masks $M$. Since the Hungarian algorithm requires
one-to-one correspondences and $M\geq M^{gt}$, we pad the ground truth mask
$M^{gt}$ with “no object” masks, which we indicate as $\phi$. The cost
function for matching $M$ and $M^{gt}$ is given by

$L_{masks}=\lambda_{bce}L_{bce}+\lambda_{dice}L_{dice}$ (1)

where $L_{bce}$ and $L_{dice}$ are, respectively, the binary cross entropy
loss and the dice loss calculated between the matched masks. The weights
$\lambda_{bce}$ and $\lambda_{dice}$ are both set to $5.0$. Additionally, we
also train the model on cross-entropy loss $L_{ce}$ to learn the semantic
class of each mask that is denoted by $C$. The total training loss is given
by:

$L=L_{masks}+\lambda_{ce}L_{ce}$ (2)

with $\lambda_{ce}$ set to 2.0 for the prediction that is matched with the
ground truth and 0.1 for $\phi$, i.e., for no object. At inference time, the
segmentation output is inferred by marginalization over the softmax of $C$ and
sigmoid of $M$ given as:

$g(x)=\max^{Z}\left(\text{softmax}(C)^{T}\cdot\text{sigmoid}(M)\right)$ (3)

In the subsequent sections we will address the tasks of anomaly segmentation
(Sec. 4), open-set semantic segmentation (Sec. 5), and open-set panoptic
segmentation (Sec. 6) using our proposed Mask2Anomaly architecture and delve
into its novel elements.

Figure 3: Mask2Anomaly Overview. Mask2Anomaly meta-architecture consists of an
encoder, a pixel decoder, and a transformer decoder. We propose GMA: Global
Mask Attention that is discussed in Sec. 4.2 and Fig. 4. $\phi$ is image
features. $\phi^{i},\phi^{i+1},\phi^{i+2}$ are upsampled image features at
multiple scales. Mask contrastive Loss $L_{CL}$ (Sec. 4.3) utilizes outlier
masks to maximize the separation between anomalies and known classes. During
anomaly inference, we utilize refinement mask $R_{M}$ (Sec. 4.4) to minimize
false positives.

##  4 Anomaly Segmentation

###  4.1 Problem Setting

Anomaly segmentation can be achieved in per-pixel semantic segmentation
architectures [12] by applying the Maximum Softmax Probability (MSP) [30] on
top of the per-pixel classifier. Formally, given the pixel-wise class scores
$S(x)\in[0,1]^{Z\times H\times W}$ obtained by segmenting the image $x$ with a
per-pixel architecture, we can compute the anomaly score $f(x)$ as:

$f(x)=1-\max^{Z}(S(x)).$ (4)

In this paper, we propose to adapt this framework based on MSP for mask-
transformer segmentation architectures. Given such a mask-transformer
architecture, we calculate the anomaly scores for an input $x$ as

$f(x)=1-\max^{Z}\left(\text{softmax}(C)^{T}\cdot\text{sigmoid}(M)\right).$ (5)

Here, $f(x)$ utilizes the same marginalization strategy of class and mask
pairs as [14] to get anomaly scores. Without loss of generality, we implement
the anomaly scoring (Eq. 5) on top of the Mask2Former [13] architecture.
However, this strategy hinges on the fact that the masks predicted by the
segmentation architecture can capture anomalies well. We found that simply
applying the MSP on top of Mask2Former as in Eq. 5 does not yield good results
(see Fig. 1 and the results in Sec. 7.5). To overcome this problem, we
introduce improvements in the architecture, training procedure, and anomaly
inference mechanism. We name our method as Mask2Anomaly, and its overview is
shown in Fig. 3 (left). Now, we will discuss the proposed novel components in
Mask2Anomaly.

Figure 4: Global Mask Attention: independently distributes the attention
between foreground and background. V, K, and Q are Value, Key, and Query.

###  4.2 Global Masked Attention

One of the key ingredients to Mask2Former [13] state-of-the-art segmentation
results is the replacement of the cross-attention (CA) layer in the
transformer decoder with a masked-attention (MA). The masked-attention attends
only to pixels within the foreground region of the predicted mask for each
query, under the hypothesis that local features are enough to update the query
object features. The output of the $l$-th masked-attention layer can be
formulated as

$\text{softmax}(\mathcal{M}^{F}_{l}+QK^{T})V+X_{in}$ (6)

where $X_{in}\in$ $\mathbb{R}^{N\times C}$ are the $N$ $C$-dimensional query
features from the previous decoder layer. The queries $Q\in\mathbb{R}^{N\times
C}$ are obtained by linearly transforming the query features with a learnable
transformation whereas the keys and values $K,V$ are the image features under
learnable linear transformations $f_{k}(.)$ and $f_{v}()$. Finally,
$\mathcal{M}_{l}^{F}$ is the predicted foreground attention mask that at each
pixel location $(i,j)$ is defined as

$\mathcal{M}_{l}^{F}(i,j)=\begin{cases}0&\text{if }M_{l-1}(i,j)\geq 0.5\\\
-\infty&\text{otherwise},\end{cases}$ (7)

where $M_{l-1}$ is the output mask of the previous layer.

By focusing only on the foreground objects, masked attention grants faster
convergence and better semantic segmentation performance than cross-attention.
However, focusing only on the foreground region constitutes a problem for
anomaly segmentation because anomalies may also appear in the background
regions. Removing background information leads to failure cases in which the
anomalies in the background are entirely missed, as shown in the example in
Fig. 5. To ameliorate the detection of anomalies in these corner cases, we
extend the masked attention with an additional term focusing on the background
region (see Fig. 4, right). We call this a global masked-attention (GMA)
formally expressed as

$\displaystyle X_{out}=$
$\displaystyle\text{softmax}(\mathcal{M}_{l}^{F}+QK^{T})V$ (8)
$\displaystyle+$
$\displaystyle\text{softmax}(\mathcal{M}_{l}^{B}+QK^{T})V+X_{in}$

where $\mathcal{M}_{l}^{B}$ is the additional background attention mask that
complements the foreground mask $\mathcal{M}_{l}^{F}$, and it is defined at
the pixel coordinates $(i,j)$ as

$\mathcal{M}_{l}^{B}(i,j)=\begin{cases}0&\text{if }M_{l-1}(i,j)<0.5\\\
-\infty&\text{otherwise}.\end{cases}$ (9)

The global masked-attention in Eq. 8 differs from the masked-attention by
additionally attending to the background mask region, yet it retains the
benefits of faster convergence w.r.t. the cross-attention.

Input Image Attention Map Ground Truth

Figure 5: Limitation of Mask-Attention: Masked-attention [13] selectively
attends to foreground regions resulting in low attention scores (dark regions)
for anomalies. Anomalies are in red. Best viewed with Zoom.

###  4.3 Mask Contrastive Learning

The ideal characteristic of an anomaly segmentation model is to predict high
anomaly scores for out-of-distribution (OOD) objects and low anomaly scores
for in-distribution (ID) regions. Namely, we would like to have a significant
margin between the likelihood of known classes being predicted at anomalous
regions and vice-versa. A common strategy used to improve this separation is
to fine-tune the model with auxiliary out-of-distribution (anomalous) data as
supervision [25, 26, 5].

Here we propose a contrastive learning approach to encourage the model to have
a significant margin between the anomaly scores for in-distribution and out-
of-distribution classes. Our mask-based framework allows us to
straightforwardly implement this contrastive strategy by using as supervision
outlier images generated by cutting anomalous objects from the auxiliary OOD
data and pasting it on top of the training data. For each outlier image, we
can then generate a binary outlier mask $M_{OOD}$ that is $1$ for out-of-
distribution pixels and $0$ for in-distribution class pixels. With this
setting, we first calculate the negative likelihood of in-distribution classes
using the class scores $C$ and class masks $M$ as:

$\displaystyle
l_{N}=-\max^{Z}\left(\text{softmax}(C)^{T}\cdot\text{sigmoid}(M)\right)$ (10)

Ideally, for pixels corresponding to in-distribution classes $l_{N}$ should be
$-1$ since the value of $\text{softmax}(C)^{T}$ and $\text{sigmoid}(M)$ would
be close to $1$. On the other hand, for the anomalous pixels, $l_{N}$ should
be $0$ as the likelihood of these pixels belonging to any in-distribution
classes is $0$ resulting $\text{softmax}(C)^{T}$ to be $0$. Using $l_{N}$, we
define our contrastive loss as:

$\displaystyle L_{CL}$ $\displaystyle=\frac{1}{2}(l_{CL}^{2}),$ (11)
$\displaystyle l_{CL}$ $\displaystyle=\begin{cases}l_{N}&\text{if}M_{OOD}=0\\\
max(0,m-l_{N})&\text{otherwise,}\end{cases}$

where the margin $m$ is a hyperparameter that decides the minimum distance
between the out-of-distribution and in-distribution classes. During mask
contrastive training, we also preserve the in-distribution accuracy by
training on $L_{masks}$ and $L_{ce}$ which formulates our total training loss
as:

$L_{ood}=L_{CL}+L_{masks}+\lambda_{ce}L_{ce}$ (12)

Figure 6: Mask Refinement Illustration: To obtain the refined prediction, we
multiply the prediction map with a refinement mask that is built by assigning
zero anomaly scores for pixels that are categorized as “stuff”, except for the
“road”. The refinement eliminates many false positives at the boundary of
objects and in the background. The region to be masked is white in the
refinement mask.

###  4.4 Refinement Mask

False positives are one of the main problems in anomaly segmentation,
particularly around object boundaries. Handcrafted methods such as iterative
boundary suppression [33] or dilated smoothing have been proposed to minimize
the false positives at boundaries or globally, however, they require tuning
for each specific dataset. Instead, we propose a general refinement technique
that leverages the capability of mask transformers [13] to perform all
segmentation tasks. Our method stems from the panoptic perspective [34] that
the elements in the scene can be categorized as things, i.e. countable
objects, and stuff, i.e. amorphous regions. With this distinction in mind, we
observe that in driving scenes, i) unknown objects are classified as things,
and ii) they are often present on the road. Thus, we can proceed to remove
most false positives by filtering out all the masks corresponding to “stuff”,
except the “road” category. We implement this removal mechanism in the form of
a binary refinement mask $R_{M}\in[0,1]^{H\times W}$, which contains zeros in
the segments corresponding to the unwanted “stuff” masks and one otherwise.
Thus, by multiplying $R_{M}$ with the predicted anomaly scores $f$ we filter
out all the unwanted “stuff” masks and eliminate a large portion of the false
positives (see Fig. 6). Formally, for an image $x$ the refined anomaly scores
$f^{r}$ is computed as:

$f^{r}(x)=R_{M}\odot f(x),$ (13)

where $\odot$ is the Hadamard product.

$R_{M}$ is the dot product between the binarized output mask
$\bar{M}\in\\{0,1\\}^{N\times(H\times W)}$ and the class filter
$\bar{C}\in\\{0,1\\}^{1\times N}$, i.e. $R_{M}=\bar{C}\cdot\bar{M}$. We define
$\bar{M}=\text{sigmoid}(M)>0.5$ and the class filter $\bar{C}$ is equal to $1$
only where the highest class score of $\text{softmax}(C)$ belongs to “things”
or “road” classes and is greater than $0.95$.

Inference: During inference, we pass the input image through Mask2Anomaly to
get anomaly scores Eq. 5. Then, we refine the anomaly scores via refinement
mask Eq. 13.

##  5 Open-Set Semantic Segmentation

###  5.1 Problem Setting

Anomaly segmentation methods solely focus on segmenting road scene anomalies.
However, a strong performance for in-distribution classes is equally
important. For instance, an anomaly segmentation model deployed in an
autonomous vehicle that fails to identify a person crossing the road can
result in a fatal accident. Hence, it is crucial that while recognizing
anomalies, the performance of the model on in-distribution classes remains
preserved. Open-set semantic segmentation addresses this problem by jointly
accessing the model’s performance on in-distribution and out-of-distribution
classes. We utilize the mask properties of Mask2Anomaly to perform open-set
semantic segmentation by only modifying its inference process with respect to
the Anomaly Segmentation task.

Inference: Our open-set semantic segmentation network has an identical mask
architecture as anomaly segmentation that contains global mask attention.
During the inference, we first threshold the anomaly scores obtained from Eq.
5 at a true positive rate of 95%, similar to [26]. We denote the thresholded
anomaly scores by $\hat{f(x)}$. Next, we calculate the in-distribution class
performance $g(x)$ by Eq. 3. Finally, we formulate the open-set semantic
segmentation $f_{oss}$ prediction of an image $x$ as:

$f_{oss}(x)=\arg\max(\text{concat}(g(x),\hat{f(x)}))$ (14)

##  6 Open Set Panoptic Segmentation

###  6.1 Problem Overview

Panoptic segmentation [34] jointly addresses the dense prediction task of
semantic segmentation and instance segmentation. In this task, we divide an
image into two broad categories: i) stuff, i.e., amorphous areas of an image
that have homogeneous texture, such as grass and sky, and ii) things, i.e.,
countable objects such as pedestrians. Every pixel belonging to a things
category is assigned a semantic label and a unique instance id, whereas, for
stuff regions, only semantic labels are given, and the instance id is ignored.
However, constructing and annotating large-scale panoptic segmentation
datasets is expensive and requires significant human effort. Hwang [32]
addresses this problem by formulating it as open-set panoptic segmentation
(OPS) problem where a model can perform panoptic segmentation on a pre-defined
set of classes and identify unknown objects. This ability of the OPS model
could accelerate the process of constructing large-scale panoptic segmentation
datasets from existing ones.

###  6.2 Problem Setting

The key difference between panoptic and open-set panoptic segmentation is the
presence of unknown objects while testing. However, handling the
classification of unknown object is OPS is quite challenging. Firstly, in
comparison to open-set image classification, OPS requires the classification
of unknown objects at the pixel level. Secondly, the absence of semantic
information about unknown objects means that they are generically labeled as
background during training. In order to make the problem tractable, we follow
[32] and make three assumptions:

  1. 1.

we categorize all the unknowns into things categories (i.e., the unknowns are
countable objects);

  2. 2.

elements of known categories cannot be classified as unknown classes;

  3. 3.

the unknown objects are always found in the background/void regions. This
avoids confusion between known and unknown class regions.

We address open-set panoptic segmentation by utilizing the mask properties of
Mask2Anomaly and leveraging its global mask attention. We first mask out the
known stuff and things regions of an image, and then within the remaining
background area, we mine the instances of the unknown objects. We will now
formally discuss the method in more detail. For an input image $x$,
Mask2Anomaly outputs a set of masks $M$ and its corresponding class scores
$C$. Among these, we denote the joint set of known stuff and things class
masks as $M_{k}\in[0,1]^{N_{k}\times H\times W}$ and its corresponding class
scores as $C_{k}\in\mathbb{R}^{N_{k}\times Z}$. Finally, we denote the number
of known class masks as $N_{k}$. We obtain the background region $\mathcal{B}$
of $x$ by using the weighted combination of $M_{k}$ and $C_{k}$ given by:

$\mathcal{B}=1-\max^{N_{k}}(\max^{Z}(\text{softmax}(C_{k}))\cdot\text{sigmoid}(M_{k})).$
(15)

In light of our assumptions, $\mathcal{B}$ consists of background stuff
classes and unknown things classes.

###  6.3 Mining Unknown Instances

Generally in panoptic segmentation datasets such as MS-COCO [39] the
background class consists of only background stuff classes. However, in open-
set panoptic segmentation, the background class consists of background stuff
classes and unknown things classes. So, we mine the unknown instances from
background $\mathcal{B}$ obtained from Eq. 15 using the following steps:

  1. 1.

In the first step, we employ the connected component algorithm [4] to cluster
and identify unique segments in $\mathcal{B}$.

  2. 2.

Next, we calculate each connected component’s overlap with the individual
masks of $M$. Intersection over union is used for calculating the overlap.

  3. 3.

If there is a significant overlap between a connected component and a mask
$M^{i}\in M$, we calculate the average stuff class entropy $\mathcal{E}_{S}$
and average things class entropy $\mathcal{E}_{T}$ using the corresponding
class scores $C^{i}\in C$.

  4. 4.

Finally, if $\mathcal{E}_{S}>\mathcal{E}_{T}$ we can conclude that the
connected component is more likely to belong to the things class. Hence, we
classify the connected component to be an unknown instance.

Inference: During the inference, we first calculate $\mathcal{B}$ from Eq. 15.
Then, we identify the unknown instances in $\mathcal{B}$ by following the
above described steps of mining unknown instances.

##  7 Experimentation

###  7.1 Datasets

Anomaly Segmentation: We train Mask2Anomaly on the Cityscapes [15] dataset,
which consists of 2975 training and 500 validation images. To evaluate anomaly
segmentation, we use Road Anomaly [40], Lost & Found [48], Fishyscapes [5],
and Segment Me If You Can (SMIYC) benchmarks [10]. Road Anomaly: is a
collection of 60 web images with anomalous objects on or near the road. Lost &
Found: has 1068 test images with small obstacles for road scenes. Fishyscapes
(FS): consists of two datasets, Fishyscape static (FS static) and Fishyscapes
lost & found (FS lost & found). Fishyscapes static is built by blending Pascal
VOC [21] objects on Cityscapes images containing 30 validation and 1000 test
images. Fishyscapes lost & found is based on a subset of the Lost and Found
dataset [48], with 100 validation and 275 test images. SMIYC: consists of two
datasets, RoadAnomaly21 (SMIYC-RA21) and RoadObstacle21 (SMIYC-RO21). The
SMIYC-RA21 contains 10 validation and 100 test images with diverse anomalies.
The SMIYC-RO21 is collected to segment road anomalies and has 30 validation
and 327 test images.

Open-set panoptic segmentation: We perform all the open-set panoptic
segmentation experiments on the panoptic segmentation dataset of MS-COCO [39].
The dataset consists of 118 thousand training images and 5 thousand validation
images having 80 thing classes and 53 stuff classes. We construct open-set
panoptic segmentation dataset by removing the labels of a small set of known
things classes from the train set of panoptic segmentation dataset. The
removed set of things classes are treated as unknown classes. We construct
three different training dataset split with increasing order of difficulty
with (5%, 10%, 20%) of unknown classes. The removed classes in each split that
are removed cumulatively is given as: 5%: {car, cow, pizza, toilet}, 10%:
{boat, tie, zebra, stop sign }, 20%: {dining table, banana, bicycle, cake,
sink, cat, keyboard, bear}.

Open-set semantic segmentation: We use StreetHazards [29], a synthetic dataset
for open-set semantic segmentation. StreetHazards dataset is created with the
CARLA simulator [18], leveraging the Unreal Engine to render realistic road
scene images in which diverse anomalous objects are inserted. The dataset
consists of 5125 training images and 1031 validation images having 12 classes.
The test set has 1500 images along with an additional anomaly class.

###  7.2 Evaluation Metrics

Anomaly Segmentation: We evaluate all the anomaly segmentation methods at
pixel and component levels that are described next.

_Pixel-Level_ : For pixel-wise evaluation, $Y\in\\{Y_{a},Y_{na}\\}$ is the
pixel level annotated ground truth labels for an image $\chi$ containing
anomalies. $Y_{a}$ and $Y_{na}$ represents the anomalous and non-anomalous
labels in the ground-truth, respectively. Assume that $\hat{Y}(\gamma)$ is the
model prediction obtained by thresholding at $\gamma$. Then, we can write the
precision and recall equations as

$\text{precision}(\gamma)=\frac{|Y_{a}\cap\hat{Y}_{a}(\gamma)|}{|\hat{Y}_{a}(\gamma)|}$
(16)

$\text{recall}(\gamma)=\frac{|Y_{a}\cap\hat{Y}_{a}(\gamma)|}{|Y_{a}|}$ (17)

and the AuPRC can be approximated as

$\text{AuPRC}=\int_{\gamma}\text{precision}(\gamma)\text{recall}(\gamma)$ (18)

The AuPRC works well for unbalanced datasets making it particularly suitable
for anomaly segmentation since all the datasets are significantly skewed.
Next, we consider the False Positive Rate at a true positive rate of 95%
(FPR95), an important criterion for safety-critical applications that is
calculated as:

$\text{FPR}_{95}=\frac{|\hat{Y}_{a}(\gamma^{*})\cap Y_{na}|}{|Y_{na}|}$ (19)

where $\gamma^{*}$ is a threshold when the true positive rate is 95%.

_Component-Level_ : SMIYC [10] introduced component-level evaluation metrics
that solely focus on detecting anomalous objects regardless of their size.
These metrics are important to be considered because pixel-level metrics may
not penalize a model for missing a small anomaly, even though such a small
anomaly may be important to be detected. In order to have a component-level
assessment of the detected anomalies, the quantities to be considered are the
component-wise true-positives ($TP$), false-negatives ($FN$), and false-
positives ($FP$). These component-wise quantities can be measured by
considering the anomalies as the positive class. From these quantities, we can
use three metrics to evaluate the component-wise segmentation of anomalies:
sIoU, PPV, and F1∗. Here we provide the details of how these metrics are
computed, using the notation $\mathcal{K}$ to denote the set of ground truth
components, and $\hat{\mathcal{K}}$ to denote the set of predicted components.

The sIoU metric used in SMIYC [10] is a modified version of the component-wise
intersection over union proposed in [50], which considers the ground-truth
components in the computation of the $TP$ and $FN$. Namely, it is computed as

$\text{sIoU}(k)=\frac{|k\cap\hat{K}(k)|}{|k\cap\hat{K}(k)\backslash\mathcal{A}(k)|},\qquad\hat{K}(k)=\bigcup_{\hat{k}\in\hat{\mathcal{K}},\,\hat{k}\cap
k\neq\emptyset}\hat{k}$ (20)

where $\mathcal{A}(k)$ is an adjustment term that excludes from the union
those pixels that correctly intersect with another ground-truth component
different from $k$. Given a threshold $\tau\in[0,1]$, a target
$k\in\mathcal{K}$ is considered a $TP$ if $sIoU(k)>\tau$, and a $FN$
otherwise.

The positive predictive value (PPV) is a metric that measures the $FP$ for a
predicted component $\hat{k}\in\hat{\mathcal{K}}$, and it is computed as

$\text{PPV}(\hat{k})=\frac{|\hat{k}\cap\hat{K}(k)|}{|\hat{k}|}$ (21)

A predicted component $\hat{k}\in\hat{\mathcal{K}}$ is considered a $FP$ if
$PPV(\hat{k})\leq\tau$. Finally, the $F1^{*}$ summarizes all the component-
wise $TP$, $FN$, and $FP$ quantities by the following formula:

$F1^{*}(\tau)=\frac{2TP(\tau)}{2TP(\tau)+FN(\tau)+FP(\tau)}$ (22)

Open-set semantic segmentation: We use open-IoU [26] to evaluate open-set
semantic segmentation. Unlike, IoU, open-IoU takes into account the false
positives ($FP^{OOD}$) and false negatives ($FN^{OOD}$) of an anomaly
segmentation model. To measure open-IoU, we first threshold the output of the
anomaly segmentation model at a true positive rate of 95% and then re-
calculate the classification scores of in-distribution classes according to
the anomaly threshold. Now, $FP^{OOD}$ and $FN^{OOD}$ for a class $\alpha$ can
be calculated as:

$FP_{\alpha}^{OOD}=\sum_{i=1,i\neq\alpha}^{Z+1}FP_{\alpha}^{i},FN_{\alpha}^{OOD}=\sum_{i=1,i\neq\alpha}^{Z+1}FN_{\alpha}^{i}$
(23)

Using $FP_{\alpha}^{OOD}$ and $FN_{\alpha}^{OOD}$, we can calculate the open-
IoU for class $\alpha$ as:

$\text{open-
IoU}_{\alpha}=\frac{TP_{\alpha}}{TP_{\alpha}+FP_{\alpha}^{OOD}+FN_{\alpha}^{OOD}}$
(24)

$TP_{\alpha}$ denotes the true-positive of class $\alpha$. An ideal open-set
model will have open-IoU to be equal to IoU.

Open-set panoptic segmentation: We measure the panoptic segmentation quality
of known and unknown classes by using the panoptic quality ($PQ$) metric [34].
For each class, $PQ$ is calculated individually and averaged over all the
classes making $PQ$ independent of class imbalance. Every class has predicted
segments $p$ and its corresponding ground truths $g$ that is divided into
three parts: true positives ($TP$): matched pair of segments, false positives
($FP$): unmatched predicted segments, and false negatives ($FN$): unmatched
ground truth segments. Given the three sets, $PQ$ can be formulated as:

$PQ=\underbrace{\frac{\sum_{(p,g)\in TP}IoU(p,g)}{|TP|}}_{\text{segmentation
quality
(SQ)}}\times\underbrace{\frac{|TP|}{|TP|+\frac{1}{2}|FP|+\frac{1}{2}|FN|}}_{\text{recognition
quality (RQ)}}$ (25)

From the above equation, we can see $PQ$ as the product of a segmentation
quality ($SQ$) and a recognition quality ($RQ$). $RQ$ can be inferred as an F1
score that gives the estimation of segmentation quality. $SQ$ is the average
IoU of matched segments.

###  7.3 Implementation Details

Anomaly Segmentation: Our implementation is derived from [14, 13]. We use a
ResNet-50 [28] encoder, and its weights are initialized from a model that is
pre-trained with barlow-twins [59] self-supervision on ImageNet [16]. We
freeze the encoder weights during training, saving memory and training time.
We use a multi-scale deformable attention Transformer (MSDeformAttn) [64] as
the pixel decoder. The MSDeformAttn gives features maps at $1/8,1/16,$ and
$1/32$ resolution, providing image features to the transformer decoder layers.
Our transformer decoder is adopted from [13] and consists of 9 layers with 100
queries. We train Mask2Anomaly using a combination of binary cross-entropy
loss and the dice loss [45] for class masks and cross-entropy loss for class
scores. The network is trained with an initial learning rate of 1e-4 and batch
size of 16 for 90 thousand iterations on AdamW [43] with a weight decay of
0.05. We use an image crop of $380\times 760$ with large-scale jittering [20]
along with a random scale ranging from 0.1 to 2.0. Next, we train the
Mask2Anomaly in a contrastive setting. We generate the outlier image using
AnomalyMix [54] where we cut an object from MS-COCO [39] dataset image and
paste them on the Cityscapes image. The corresponding binary mask for an
outlier image is created by assigning $1$ to the MS-COCO image area and $0$ to
the Cityscapes image area. We randomly sample 300 images from the MS-COCO
dataset during training to generate outliers. We train the network for 4000
iterations with $m$ as 0.75, a learning rate of 1e-5, and batch size 8,
keeping all the other hyper-parameters the same as above. The probability of
choosing an outlier in a training batch is kept at 0.2.

| SMIYC RA-21 | SMIYC RO-21 | FS L&F | FS Static | Road Anomaly | Lost & Found | Average
---|---|---|---|---|---|---|---
Methods | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95$\downarrow$ | AuPRC $\uparrow$ | FPR95$\downarrow$
Max Softmax [30](ICLR’17) | 27.97 | 72.02 | 15.72 | 16.6 | 1.77 | 44.85 | 12.88 | 39.83 | 15.72 | 71.38 | 30.14 | 33.20 | 17.36 | 46.31
Entropy [30](ICLR’17) | - | - | - | - | 2.93 | 44.83 | 15.4 | 39.75 | 16.97 | 71.1 | - | - | 11.76 | 51.89
Mahalanobis [36](NeurIPS’18) | 20.04 | 86.99 | 20.9 | 13.08 | - | - | - | - | 14.37 | 81.09 | 54.97 | 12.89 | 27.57 | 48.51
Image Resynthesis [40](ICCV’19) | 52.28 | 25.93 | 37.71 | 4.7 | 5.7 | 48.05 | 29.6 | 27.13 | - | - | 57.08 | 8.82 | 36.47 | 22.92
Learning Embedding [5](IJCV’21) | 37.52 | 70.76 | 0.82 | 46.38 | 4.65 | 24.36 | 57.16 | 13.39 | - | - | 61.70 | 10.36 | 32.37 | 33.05
Void Classifier [5](IJCV’21) | 36.61 | 63.49 | 10.44 | 41.54 | 10.29 | 22.11 | 4.5 | 19.4 | - | - | 4.81 | 47.02 | 13.33 | 38.71
JSRNet [56](ICCV’21) | 33.64 | 43.85 | 28.09 | 28.86 | - | - | - | - | 94.4 | 9.2 | 74.17 | 6.59 | 57.57 | 22.12
SML [33](ICCV’21) | 46.8 | 39.5 | 3.4 | 36.8 | 31.67 | 21.9 | 52.05 | 20.5 | 17.52 | 70.7 | - | - | 30.28 | 37.88
SynBoost [17](CVPR’21) | 56.44 | 61.86 | 71.34 | 3.15 | 43.22 | 15.79 | 72.59 | 18.75 | 38.21 | 64.75 | 81.71 | 4.64 | 60.58 | 28.15
Maximized Entropy [11](ICCV’21) | 85.47 | 15.00 | 85.07 | 0.75 | 29.96 | 35.14 | 86.55 | 8.55 | 48.85 | 31.77 | 77.90 | 9.70 | 68.96 | 16.81
Dense Hybrid [26](ECCV’22) | 77.96 | 9.81 | 87.08 | 0.24 | 47.06 | 3.97 | 80.23 | 5.95 | 31.39 | 63.97 | 78.67 | 2.12 | 67.06 | 14.34
PEBEL [54](ECCV’22) | 49.14 | 40.82 | 4.98 | 12.68 | 44.17 | 7.58 | 92.38 | 1.73 | 45.10 | 44.58 | 57.89 | 4.73 | 48.94 | 18.68
Mask2Anomaly (Ours) | 88.70 | 14.60 | 93.30 | 0.20 | 46.04 | 4.36 | 95.20 | 0.82 | 79.70 | 13.45 | 86.59 | 5.75 | 81.59 | 6.53

TABLE I: Pixel level evaluation: On average, Mask2Anomaly shows significant
improvement among the compared methods. Higher values for AuPRC are better,
whereas for FPR95 lower values are better. The best and second best results
are bold and underlined, respectively. ‘-’ indicates the unavailability of
benchmark results.  Figure 7: Semantic segmentation results: We can visually
infer that Mask2Anomaly shows similar segmentation results when compared with
Mask2Former [13].

Open-set semantic segmentation: We the use Streethazard [29] dataset to train
Mask2Anomaly with a Swin-Base backbone. The model was trained for 50 thousand
iterations keeping all the parameters the same as anomaly segmentation. Next,
we train Mask2Anomaly on outlier images in a contrastive settings. The outlier
image was created by AnomalyMix [54] using MS-COCO [39] and Streethazard
image. We train the network for 5000 iterations keeping the Swin-Base backbone
frozen. The image crop size was kept at $380\times 760$, the rest all the
other hyper-parameters were the same as for anomaly segmentation.

Open-set panoptic segmentation: We train Mask2Anomaly having ResNet-50
backbone for 370 thousand iterations. Our training approach employs a batch
size of 8, incorporating cropped input images sized at 640$\times$640\. We
keep the remaining hyperparameters same as specified in the anomaly
segmentation. Across all the three training datasets, which contain 5%, 10%,
and 15% of unknown classes, the number of connected components were 2, 2, and
3 respectively. The number of iteration for connected component algorithm was
kept at 500 for each training dataset.

| SMIYC RA-21 | SMIYC RO-21 | Lost & Found | Average
---|---|---|---|---
Methods | sIoU $\uparrow$ | PPV $\uparrow$ |  $F1^{*}$$\uparrow$ | sIoU $\uparrow$ | PPV $\uparrow$ |  $F1^{*}$$\uparrow$ | sIoU $\uparrow$ | PPV $\uparrow$ |  $F1^{*}$$\uparrow$ | sIoU $\uparrow$ | PPV $\uparrow$ |  $F1^{*}$$\uparrow$
Max Softmax [30](ICLR’17) | 15.48 | 15.29 | 5.37 | 19.72 | 15.93 | 6.25 | 14.20 | 62.23 | 10.32 | 16.47 | 31.15 | 7.31
Ensemble [35](NurIPS’17) | 16.44 | 20.77 | 3.39 | 8.63 | 4.71 | 1.28 | 6.66 | 7.64 | 2.68 | 10.58 | 11.04 | 2.45
Mahalanobis [36](NeurIPS’18) | 14.82 | 10.22 | 2.68 | 13.52 | 21.79 | 4.70 | 33.83 | 31.71 | 22.09 | 20.72 | 21.24 | 9.82
Image Resynthesis [40](ICCV’19) | 39.68 | 10.95 | 12.51 | 16.61 | 20.48 | 8.38 | 27.16 | 30.69 | 19.17 | 27.82 | 20.71 | 13.35
MC Dropout [46](CVPR’20) | 20.49 | 17.26 | 4.26 | 5.49 | 5.77 | 1.05 | 17.35 | 34.71 | 12.99 | 14.44 | 19.25 | 6.10
Learning Embedding [5](IJCV’21) | 33.86 | 20.54 | 7.90 | 35.64 | 2.87 | 2.31 | 27.16 | 30.69 | 19.17 | 32.22 | 18.03 | 9.79
SML [33](ICCV’21) | 26.00 | 24.70 | 12.20 | 5.10 | 13.30 | 3.00 | 32.14 | 27.57 | 26.93 | 21.08 | 21.86 | 14.04
SynBoost [17](CVPR’21) | 34.68 | 17.81 | 9.99 | 44.28 | 41.75 | 37.57 | 36.83 | 72.32 | 48.72 | 38.60 | 43.96 | 32.09
Maximized Entropy [11](ICCV’21) | 49.21 | 39.51 | 28.72 | 47.87 | 62.64 | 48.51 | 45.90 | 63.06 | 49.92 | 47.66 | 55.07 | 42.38
JSRNet [56](ICCV’21) | 20.20 | 29.27 | 13.66 | 18.55 | 24.46 | 11.02 | 34.28 | 45.89 | 35.97 | 24.34 | 33.21 | 20.22
Void Classifier [5](IJCV’21) | 21.14 | 22.13 | 6.49 | 6.34 | 20.27 | 5.41 | 1.76 | 35.08 | 1.87 | 9.75 | 25.83 | 4.59
Dense Hybrid [26](ECCV’22) | 54.17 | 24.13 | 31.08 | 45.74 | 50.10 | 50.72 | 46.90 | 52.14 | 52.33 | 48.94 | 42.12 | 44.71
PEBEL [54](ECCV’22) | 38.88 | 27.20 | 14.48 | 29.91 | 7.55 | 5.54 | 33.47 | 35.92 | 27.11 | 34.09 | 23.56 | 15.71
Mask2Former [13] | 25.20 | 18.20 | 15.30 | 5.00 | 21.90 | 4.80 | 17.88 | 18.09 | 9.77 | 16.03 | 19.40 | 9.96
Mask2Anomaly (Ours) | 60.40 | 45.70 | 48.60 | 61.40 | 70.30 | 69.80 | 56.07 | 63.41 | 62.78 | 59.29 | 59.80 | 60.39

TABLE II: Anomaly segmentation component level evaluation: Mask2Anomaly
achieves large improvement on component level evaluation metrics among the
baselined methods. Higher values of sIoU, PPV, and $F1^{*}$ are better. The
best and second best results are bold and underlined, respectively.

| Anomaly Segmentation | Close Set Performance | Open Set Performance
---|---|---|---
Methods | AuPRC $\uparrow$ | FPR95 $\downarrow$ | mIoU$\uparrow$ | Open-IoUt5 $\uparrow$ | Open-IoUt6 $\uparrow$ | Open-IoU$\uparrow$
MSP [30] (ICLR’17) | 7.5 | 27.9 | 65.0 | 32.7 | 40.2 | 35.1
ODIN [37] (ICLR’18) | 7.0 | 28.7 | 65.0 | 26.4 | 33.9 | 28.8
Outlier Exposure [31] (ICLR’19) | 14.6 | 17.7 | 61.7 | 43.7 | 44.1 | 43.8
OOD-Head [2] (GCPR’19) | 19.7 | 56.2 | 66.6 | 33.7 | 34.3 | 33.9
MC Dropout [46] (CVPR’20) | 7.5 | 79.4 | - | - | - | -
SynthCP [57] (ECCV’20) | 9.3 | 28.4 | - | - | - | -
TRADI [24] (ECCV’20) | 7.2 | 25.3 | - | - | - | -
OVNNI [23] (CoRR’20) | 12.6 | 22.2 | 54.6 | - | - | -
Energy [41] (NurIPS’20) | 12.9 | 18.2 | 63.3 | 41.7 | 44.9 | 42.7
PAnS [22] (CVPRW’21) | 8.8 | 23.2 | - | - | - | -
SO+H [25] (VISIGRAPP’21) | 12.7 | 22.2 | 59.7 | - | - | -
DML [7] (ICCV’21) | 14.7 | 17.3 | - | - | - | -
ReAct [52] (NurIPS’21) | 10.9 | 21.2 | 62.7 | 33.0 | 36.2 | 34.0
OH*MSP [3] (CoRR’21) | 18.8 | 30.9 | 66.6 | 43.3 | 44.2 | 43.6
ML [29] (ICML’22) | 11.6 | 22.5 | 65.0 | 39.6 | 44.5 | 41.2
DenseHybrid [26] (ECCV’22) | 30.2 | 13.0 | 63.0 | 46.1 | 45.3 | 45.8
Mask2Anomaly | 58.1 | 14.9 | 72.3 | 59.9 | 59.7 | 59.8

TABLE III: Open-set semantic segmentation quantitative evaluation: We observe
that Mask2Anomaly achieves the best performance on open and close set semantic
segmentation matrices. ‘-’ indicates the unavailability of benchmarked
results.

Ground Truth Mask2Anomaly Maximized Entropy [11] Dense Hybrid [26] Input Image

Figure 8: Anomaly segmentation qualitative results: We observe that per-pixel
classification architectures: Dense Hybrid [26] and Maximized Entropy [11]
suffer from large false positives, whereas Mask2Anomaly, which is a mask-
transformer, shows accurate pixel-wise anomaly segmentation results.

Input Image Dense Hybrid [26] Mask2Anomaly Ground Truth

Figure 9: Qualitative results of open-set semantic segmentation: We can
observe that the Mask2Anomaly gives precise boundaries for open-set objects
compared to best performing per-pixel architecture i.e. Dense Hybrid.

Input Image EOPSN [32] Mask2Anomaly Ground Truth

Figure 10: Open-set panoptic segmentation qualitative results: Row 1-3: We can
observe that Mask2Anomaly is better able to segment the different instances of
unknown objects compared with the baselined method. Row 4: Shows that
Mask2Anomaly gives better panoptic segmentation with precise boundaries on
known classes. Figure 11: Mask2Anomaly Qualitative Ablation: demonstrates the
performance gain by progressively adding (left to right ) proposed components.
Masked-out regions by refinement mask are shown in white. Anomalies are
represented in red.

|  | Known Classes | Unknown Classes
---|---|---|---
K(%) | Methods | PQTh $\uparrow$ | SQTh $\uparrow$ | RQTh$\uparrow$ | PQSt $\uparrow$ | SQSt $\uparrow$ | RQSt$\uparrow$ | PQ $\uparrow$ | SQ $\uparrow$ | RQ $\uparrow$ | PQunk $\uparrow$ | SQunk $\uparrow$ | RQunk$\uparrow$
5 | Void-train | 43.6 | 80.4 | 52.8 | 28.2 | 71.5 | 36.0 | 37.3 | 76.7 | 45.9 | 8.6 | 72.7 | 11.8
| EOPSN [32] | 44.8 | 80.5 | 54.2 | 28.3 | 71.9 | 36.2 | 38.0 | 76.9 | 46.8 | 23.1 | 74.7 | 30.9
|  Mask2Anomaly | 53.0 | 82.8 | 63.4 | 41.3 | 80.8 | 50.1 | 48.2 | 81.9 | 57.9 | 24.3 | 78.2 | 32.1
10 | Void-train | 43.7 | 80.1 | 53.1 | 28.1 | 73.0 | 35.9 | 37.1 | 77.1 | 45.8 | 8.1 | 72.6 | 11.2
| EOPSN [32] | 44.5 | 80.6 | 53.8 | 28.4 | 71.8 | 36.2 | 37.7 | 76.8 | 46.3 | 17.9 | 76.8 | 23.3
|  Mask2Anomaly | 51.8 | 82.7 | 62.0 | 41.6 | 80.5 | 50.5 | 47.4 | 81.8 | 57.1 | 19.7 | 77.0 | 25.7
20 | Void-train | 44.1 | 80.1 | 53.5 | 27.9 | 71.6 | 35.6 | 36.8 | 76.3 | 45.4 | 7.5 | 72.9 | 10.3
| EOPSN [32] | 45.0 | 80.3 | 54.5 | 28.2 | 71.2 | 36.2 | 37.4 | 76.2 | 46.2 | 11.3 | 73.8 | 15.3
|  Mask2Anomaly | 50.8 | 81.6 | 60.8 | 40.4 | 80.8 | 49.0 | 46.1 | 81.2 | 55.5 | 14.6 | 76.2 | 19.1

TABLE IV: Open-set panoptic segmentation quantitative results: We show
quantitative results on the COCO val set by all the methods on varying known-
unknown splits. K denoted the % of unknown classes present in the dataset.
Best results are highlighted in bold.

###  7.4 Main Results

Anomaly Segmentation: Table I shows the pixel-level anomaly segmentation
results achieved by Mask2Anomaly and recent SOTA methods on Fishyscapes,
SMIYC, and Road Anomaly datasets. We can observe that Mask2Anomaly
significantly improves the average AuPRC by 20% and the FPR95 by 60% compared
to the second-best method. Another observation is that anomaly segmentation
methods based on per-pixel architecture, such as JSRNet, perform exceptionally
well on the Road Anomaly dataset. However, JSRNet does not generalize well on
other datasets. On the other hand, Mask2Anomaly yields excellent results on
all the datasets. Moreover, the property of our mask architecture to encourage
objectness rather than individual pixel anomalies, not only reduces the false
positive but also improves the localization of whole anomalies. Indeed, Tab.
II demonstrates that Mask2Anomaly outperforms all the baselined methods on
component-level evaluation metrics. To conclude, Mask2Anomaly yields state-of-
the-art anomaly segmentation performance both in pixel and component metrics.
To get a better understanding of the visual results, in Fig. 8 we visually
compare the anomaly scores predicted by Mask2Anomaly and its closest
competitors: Dense Hybrid [26] and Maximized Entropy [11]. The results from
both: Dense Hybrid and Maximized Entropy exhibit a strong presence of false
positives across the scene, particularly on the boundaries of objects
(“things”) and regions (“stuff”). On the other hand, Mask2Anomaly demonstrates
the precise segmentation of anomalies while at the same time having minimal
false positives.

Another critical characteristic of any anomaly segmentation method is that it
should not disturb the in-distribution classification performance, or else it
would make the semantic segmentation model unusable. We show that Tab. V(c)
Mask2Anomaly achieves mIoU of 80.45, consisting of only GMA as a novel
component. However, after mask contrastive training, we find that Mask2Anomaly
maintains an in-distribution accuracy of 78.88 mIoU on the Cityscapes
validation dataset, which is still 1.46 points higher than the vanilla
Mask2Former. Moreover, it is important to note that both Mask2Anomaly and
Mask2Former are trained for 90k iterations, indicating that, although
Mask2Anomaly additionally attends to the background mask region, it shows
convergence similar to Mask2Former. Fig. 7 qualitatively shows Mask2Anomaly
semantic segmentation results are almost identical to Mask2Former.

Open-set semantic segmentation: Table III illustrates the open-set semantic
segmentation performance of Mask2Anomaly on the StreetHazards test set. In
terms of anomaly segmentation performance, we observe that Mask2Anomaly gives
a significant gain of 90% compared to DenseHybrid in AuPRC with minimal
increase in false positives. Notably, Mask2Anomaly also gives the best closed
set performance, indicating its ability to improve in-distribution while
giving state-of-the-art anomaly segmentation results. Furthermore, we measure
open-set semantic segmentation using Open-IoU metrics, which allows us to
measure anomalous and in-distribution class performance jointly.

The Streethazard test dataset consists of two sets: t5 and t6. So, to
calculate Open-IoU on t5: Open-IoUt5, we select the anomaly threshold from t6
at a true positive rate of 95% and then re-calculate the classification scores
of in-distribution classes t5. We repeat the same steps to get Open-IoUt6. To
get the overall Open-IoU on the Streethazard test, we calculate the weighted
average of Open-IoU on t5 and t6 according to the number of images in each
set. In Table III, we can observe Mask2Anomaly outperforms other baselined
methods by a significant margin of 30% on Open-IoU metrics. It is also
important to note that methods such as OOD-Head achieve good close-set
performance but show low Open-IoU. On the other hand, Outlier Exposure has a
relatively better Open-IoU but losses close set performance. Mask2Anomaly does
not suffer such shortcomings and gives the best open and closed set
performances. Qualitatively from Fig. 9, we can visually infer that
Mask2Anomaly is able to preciously segment the anomalous/open-set objects as
compared to the best per-pixel architecture i.e., Dense Hybrid.

Open-set panoptic segmentation: Table IV summarises the open-set panoptic
segmentation performance of all the methods. Void-train is a baseline method
in which we train the void regions of an image by treating it as a new class.
We can observe Mask2Anomaly shows the best open-set panoptic segmentation
results among all the baselined methods on different proportions of unknown
classes. Additionally, it also shows strong results on in-distribution classes
that are indicated by various panoptic evaluation metrics. Figure 10
illustrates the qualitative comparison of Mask2Anomaly with baselined methods
on most challenging dataset having 20 % unknown classes. In Figure 10 (Row:
1-3), we can see Mask2Anomaly can better perform panoptic segmentation on
unknown instances compared with baselined methods. Figure 10 (Row: 4), shows
the panoptic segmentation on known classes where we can observe Mask2Anomaly
outputs are precise with minimal false positives.

###  7.5 Ablations

All the results reported in this section are based on the FS L&F validation
dataset.

Mask2Anomaly: Table V(a) presents the results of a component-wise ablation of
the technical novelties included in Mask2Anomaly. We use Mask2Former as the
baseline. As shown in the table, removing any individual component from
Mask2Anomaly drastically reduces the results, thus proving that their
individual benefits are complimentary. In particular, we observe that the
global masked attention has a big impact on the AuPRC and contrastive learning
is very important for the FPR95. The mask refinement brings further
improvements to both. Figure 11 visually demonstrates the positive effect of
all the components.

| GMA | CL | RM | AuPRC$\uparrow$ | FPR95$\downarrow$
---|---|---|---|---
|  |  | 10.60 | 89.35
✓ |  | ✓ | 35.05 | 87.11
| ✓ | ✓ | 57.23 | 31.93
✓ | ✓ |  | 68.95 | 24.07
✓ | ✓ | ✓ | 69.41 | 9.46

| margin($m$) | AuPRC$\uparrow$ | FPR95$\downarrow$
---|---|---
$1$ | 65.37 | 11.61
$0.95$ | 65.40 | 12.20
$0.90$ | 66.05 | 13.49
$0.80$ | 66.20 | 14.89
$0.75$ | 69.41 | 9.46
$0.50$ | 62.07 | 13.26

(a) | (b)

|  | mIoU$\uparrow$ | AuPRC$\uparrow$ | FPR95$\downarrow$
---|---|---|---
CA [14] | 76.43 | 20.30 | 89.35
MA [13] | 77.42 | 10.60 | 89.39
GMA | 80.45 | 32.35 | 25.95

|  | AuPRC$\uparrow$ | FPR95$\downarrow$
---|---|---
$w/o$ Refinement Mask | 68.95 | 24.07
$L_{\\{things\hskip 0.70004pt\setminus\hskip 0.70004ptroad\\}}$ | 67.04 | 39.11
$L_{\\{stuff\hskip 0.70004pt\setminus\hskip 0.70004ptroad\\}}$ | 69.41 | 9.46

| Batch Outlier Probability | AuPRC$\uparrow$ | FPR95$\downarrow$
---|---|---
0.1 | 63.01 | 14.66
0.2 | 69.41 | 9.46
0.5 | 69.20 | 11.03
1 | 68.77 | 10.53

(c) | (d) | (e)

TABLE V: Mask2Anomaly Ablation tables: (a) Component-wise ablation of
Mask2Anomaly. Results in italics show Mask2Former results. GMA: Global Mask
Attention, CL: Contrastive Learning, and RM: Refinement Mask. (b) Shows the
behavior of $L_{CL}$ by choosing different margin($m$) values. We empirically
find the best results when $m$ is 0.75. (c) Global masked attention (GMA)
performs the best among various attention mechanisms: Cross-Attention (CA) and
Masked-Attention (MA). It is also important to note that the derived results
do not have any additional proposed components of Mask2Anomaly apart from GMA.
(d) We show the performance gain by using a refinement mask that masks the
$\\{stuff\setminus road\\}$ regions as anomalies are categorized as $things$
class. (e) Batch outlier probability is the likelihood of selecting an outlier
image for a batch during contrastive training. The best result is achieved at
0.2 probability. (All the results reported on FS Lost & Found validation set).

CA [14] MA [13] GMA (Ours)

Figure 12: Visualization of negative attention maps and results: Global mask
attention gives high attention scores to anomalous regions across all
resolutions showing the best anomaly segmentation results among the compared
attention mechanisms. Cross-attention performs better than mask-attention but
has high false positives and low confidence prediction for the anomalous
region. Darker regions represent low attention values. Details to calculate
negative attention are given in Section:7.5.

Global Mask Attention: To better understand the effect of the global masked
attention (GMA), in Tab. V(c), we compare it to the masked-attention (MA) [13]
and cross-attention (CA) [55]. We can observe that although the MA increases
the mIoU w.r.t. the CA, it degrades all the metrics for anomaly segmentation,
thus confirming our preliminary experiment shown in Fig. 5. On the other hand,
the GMA provides improvements across all the metrics. This is confirmed
visually in Fig. 12, where we show the negative attention maps for the three
methods at different resolutions. The negative attention is calculated by
averaging all the queries (since there is no reference known object) and then
subtracting one. Note that the GMA has a high response on the anomaly (the
giraffe) across all resolutions.

Number of Iterations | PQ $\uparrow$ | SQ $\uparrow$ | RQ $\uparrow$
---|---|---|---
100 | 11.4 | 77.2 | 14.8
200 | 12.5 | 77.8 | 16.0
500 | 14.6 | 76.2 | 19.1
1000 | 10.9 | 76.1 | 14.9

TABLE VI: Connected component training iteration: We show the panoptic
segmentation performance of unknown classes with the increasing number of
iterations. We find the best performance at 500 iterations. Best results are
shown in bold.

Number of Connected Components | PQ $\uparrow$ | SQ $\uparrow$ | RQ $\uparrow$
---|---|---|---
1 | 10.9 | 76.1 | 14.9
2 | 12.4 | 76.5 | 16.3
3 | 14.6 | 76.2 | 19.1
5 | 14.0 | 78.2 | 17.9

TABLE VII: Number of connected components: Shows the panoptic segmentation
performance of unknown classes with the increasing number of connected
components. We find the best performance at 3. Best results are shown in bold.

| PQ $\uparrow$ | SQ $\uparrow$ | RQ $\uparrow$
---|---|---|---
ours | 14.6 | 76.2 | 19.1
\- mining unknown instances | 9.1 | 72.9 | 12.8
\- global mask attention | 9.0 | 72.3 | 11.3

TABLE VIII: Mining unknown instances for OPS: We show the negative impact on
panoptic segmentation performance of unknown classes by progressively
subtracting global mask attention and mining unknown instances components.

| SMIYC-RA21 | SMIYC-RO21 | FS L&F | FS Static | Average $\sigma$
---|---|---|---|---|---
Methods | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC $\uparrow$ | FPR95 $\downarrow$ | AuPRC | FPR95
Mask2Anomaly-S1 | 95.48 | 2.41 | 92.89 | 0.15 | 69.41 | 9.46 | 90.54 | 1.98 | - | -
Mask2Anomaly-S2 | 92.03 | 3.22 | 92.3 | 0.27 | 69.19 | 13.47 | 85.63 | 5.06 | - | -
$\sigma$(Mask2Anomaly) |  $\pm$ 2.44 |  $\pm$0.57 |  $\pm$0.42 |  $\pm$0.08 |  $\pm$0.16 |  $\pm$2.84 |  $\pm$3.47 |  $\pm$2.18 |  $\pm$1.62 |  $\pm$1.41
Dense Hybrid-S1 | 52.99 | 38.87 | 66.91 | 1.91 | 56.89 | 8.92 | 52.58 | 6.03 | - | -
Dense Hybrid-S2 | 60.59 | 32.14 | 79.64 | 1.01 | 47.97 | 18.35 | 54.22 | 5.24 | - | -
$\sigma$(Dense Hybrid) |  $\pm$5.37 |  $\pm$4.76 |  $\pm$9.00 |  $\pm$0.64 |  $\pm$6.31 |  $\pm$6.67 |  $\pm$1.16 |  $\pm$0.56 |  $\pm$5.46 |  $\pm$3.15

TABLE IX: Performance stability of Mask2Former: We can observe that the
average performance deviation in dense hybrid is significantly higher than
Mask2Anomaly. $\sigma$ denotes the standard deviation.

Refinement Mask: Table V(d) shows the performance gains due to the refinement
mask. We observe that filtering out the
$\\{\text{``stuff''}\setminus\text{``road''}\\}$ regions of the prediction map
improves the FPR95 by $14.61$ along with marginal improvement in AuPRC. On the
other hand, removing the $\\{\text{``things''}\setminus\text{``road''}\\}$
regions degrades the results, confirming our hypothesis that anomalies are
likely to belong to the “things” category. Figure 11 qualitatively shows the
improvement achieved with the refinement mask.

Mask Contrastive Learning: We tested the effect of the margin in the
contrastive loss $L_{CL}$, and we report these results in Tab. V(b). We find
that the best results are achieved by setting $m$ to 0.75, but the performance
is competitive for any value of $m$ in the table. Similarly, we tested the
effect of the batch outlier probability, which is the likelihood of selecting
an outlier image in a batch. The results shown in Tab. V(e) indicate that the
best performance is achieved at $0.2$, but the results remain stable for
higher values of the batch outlier probability.

Method | Backbone | AuPRC$\uparrow$ | FPR${}_{95}\downarrow$ | FLOPs$\downarrow$ | Training $\downarrow$
---|---|---|---|---|---
Parameters
Mask2Former [13] | ResNet-50 | 10.60 | 89.35 | 226G | 44M
| ResNet-101 | 9.11 | 45.83 | 293G | 63M
| Swin-T | 24.54 | 37.98 | 232G | 42M
| Swin-S | 30.96 | 36.78 | 313G | 69M
Mask2Anomaly‡ | ResNet-50 | 32.35 | 25.95 | 258G | 23M

TABLE X: Architectural Efficiency of Mask2Anomaly: Mask2Anomaly outperforms
the best performing Mask2Former architecture with Swin-S as backbone by using
almost 30% trainable parameters. Mask2Anomaly‡ only uses global mask
attention.

Mining Unknowns Instances: We quantitatively summarise the impact of mining
unknown instances in panoptic segmentation of unknown instances shown in Tab.
VIII. We can clearly observe that removing the mining of unknown instances
from Mask2Anomaly drastically reduces the performance across all the metrics.
Also, the absence of global mask attention further degrades performance.

Connected Components: Tabs. VI and VII shows the impact of connected
components hyperparameters on open-set panoptic segmentation of unknown
classes. In both tables, we train the model on dataset split having 20 % of
unknown classes. In Tab. VI, we can observe that Mask2Anomaly shows the best
performance at 500 iterations. Whereas, in Tab. VII, we achieve the best
performance when the number of connected components is set to 3.

Architectural Efficacy of Mask2Anomaly: We demonstrate the efficacy of
Mask2Anomaly by comparing it to the vanilla Mask2Former but using larger
backbones. The results in Tab. X show that despite the disadvantage,
Mask2Anomaly with a ResNet-50 still performs better than Mask2Former using
large transformer-based backbones like Swin-S. It is also important to note
that the number of training parameters for Mask2Anomaly can be reduced to
$23M$ as we use a frozen self-supervised pre-trained encoder during the entire
training, which is significantly less than all the Mask2Former variations.

##  8 Discussion

Performance stability: Employing an outlier set to train an anomaly
segmentation model presents a challenge because the model’s performance can
vary significantly across different sets of outliers. Here, we show that
Mask2Anomaly performs similarly when trained on different outlier sets. We
randomly chose two subsets of 300 MS-COCO images (S1, S2) as our outlier
dataset for training Mask2Anomaly and DenseHybrid. Table IX shows the
performance of Mask2Anomaly and Dense Hybrid trained on S1 and S2 outlier
sets, along with the standard deviation($\sigma$) in the performance. We can
observe that the variation in performance for the dense hybrid is
significantly higher than Mask2Anomaly. Specifically, in dense hybrid, the
average deviation in AuPRC is greater than 300%, and the average variation in
FPR95 is more than 200% compared to Mask2Anomaly.

Figure 13: Bridging the supervision gap: In this figure, we represent methods
that utilize outlier supervision in red, and those without outlier supervision
are in blue. We can observe Mask2Anomaly (w/o OS): Mask2Anomaly without using
outlier supervision, shows significant performance gain among anomaly
segmentation methods that do not use any extra supervision. Also, displays a
similar performance to PEBEL, which is the best per-pixel method that utilizes
additional supervision).

Reducing the supervision gap: In our previous discussion, we show models that
are trained with outlier supervision have varying performance across different
sets of outliers. So, we extend the previous discussion by demonstrating the
performance of Mask2Anomaly without reliance on outlier supervision. We
evaluate the performance of all the baselined method average over the
validation dataset of FS static, FS L&F, SMIYC-RA21 and SMIYC-RO21. Fig. 13
shows the performance of Mask2Anomaly with or without outlier supervision
names as Mask2Anomaly (w OS) and Mask2Anomaly (w/o OS), respectively. In the
plot, we can see unequivocally that Mask2Anomaly (w/o OS) significantly
reduces the anomaly segmentation performance gap between the methods with
outlier supervision and notably outperforms methods that do not use outlier
supervision.

Figure 14: Outlier Loss Comparision: During the training Mask2Anomaly, on the
outlier set, we find that incorporating a mask contrastive loss, which is a
margin-based loss function, resulted in better performance compared to the
conventional binary cross-entropy loss. These experiments were conducted on
the FS L&F validation set.

Outlier Loss: In this discussion, we will examine the efficacy of mask
contrastive loss in anomaly segmentation. We empirically demonstrate why mask
contrastive loss, a margin-based loss, performs better at anomaly segmentation
by comparing it with binary cross-entropy loss as an outlier loss. So, we
train Mask2Anomaly with $M_{OOD}$ using binary-cross entropy which equates the
outlier loss as:

$L_{BCE}=M_{OOD}\log(l_{N})+(1-M_{OOD})\log(1-l_{N})$ (26)

and, the new total loss at the outlier learning stage becomes:

$L_{ood}=L_{BCE}+L_{masks}+\lambda_{ce}L_{ce}$ (27)

$l_{N}$ is the negative likelihood of in-distribution classes calculated using
the class scores $C$ and class masks $M$. Figure 14 illustrates the anomaly
segmentation performance comparison on FS L&F validation dataset between the
Mask2Anomaly when trained with the binary cross entropy loss and mask
contrastive loss, respectively. We can observe that the mask contrastive loss
achieves a wider margin between out-of-distribution(anomaly) and in-
distribution prediction while maintaining significantly lower false positives.

Global Mask Attention: The application of global mask attention in semantic
segmentation has shown a positive impact on performance, as demonstrated in
Tab. V(c). So, we further investigate to assess the generalizability of this
positive effect on Ade20K [63] and Vistas [47]. To evaluate the possible
benefits of global mask attention, we trained the Mask2Former architecture
using both masked attention and global masked attention for 40 thousand
iterations. Mask2Former performed mIoU scores of 43.20 and 38.17 on masked
attention, while global mask attention yields better mIoU scores of 43.80
(+0.6) and 38.92 (+0.75) on Ade20K and Vistas, respectively.

Figure 15: Failure Cases: (a, b) Mask2Anomaly fail to segment trailer or
carriage as they have a similar appearance as car or bus. Mask2Anomaly
struggle to perform well in poor illumination (c) and weather conditions (d).
The white region represents the anomalies that are enclosed in red boxes.

Failure Cases: Fig. 15 illustrates the failure cases predicted by
Mask2Anomaly. It is apparent that Mask2Anomaly faces difficulties when
anomalies exhibit a resemblance to in-distribution classes like cars or buses,
as shown in in Fig. 15 (a, b). In Fig. 15 (c) shows increased false positives
around anomalies when illumination conditions are poor. Weather conditions
adversely effects Mask2Anomaly performance as seen in Fig. 15 (d). We think
that improving anomaly segmentation in such scenarios would be a promising
avenue for future research.

##  9 Conclusion

In this work, we introduce Mask2Anomaly, a universal architecture that is
designed to jointly address anomaly and open-set segmentation utilizing a mask
transformer. Mask2Anomaly incorporates a global mask attention mechanism
specifically to improve the attention mechanism for anomaly or open-set
segmentation tasks. For the anomaly segmentation task, we propose a mask
contrastive learning framework that leverages outlier masks to maximize the
distance between anomalies and known classes. Furthermore, we introduce a mask
refinement technique aimed at reducing false positives and improving overall
performance. For the open-set segmentation task, we developed a novel approach
to mine unknown instances based on mask-architecture properties. Through
extensive qualitative and quantitative analysis, we demonstrate the
effectiveness of Mask2Anomaly and its components. Our results highlight the
promising performance and potential of Mask2Anomaly in the field of anomaly
and open-set segmentation. We believe this work will open doors for a new
development of novel anomaly and open-set segmentation approaches based on
masked architecture, stimulating further advancements in the field.

## References

  * [1] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla.  Segnet: A deep convolutional encoder-decoder architecture for image segmentation.  PAMI, 2017.
  * [2] Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić.  Simultaneous semantic segmentation and outlier detection in presence of domain shift.  In GCPR, 2019.
  * [3] Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić.  Dense outlier detection and open-set recognition based on training with noisy negative images.  arXiv preprint arXiv:2101.09193, 2021.
  * [4] Andreas Bieniek and Alina Moga.  A connected component approach to the watershed segmentation.  Computational Imaging and Vision, 1998.
  * [5] Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena.  The fishyscapes benchmark: Measuring blind spots in semantic segmentation.  IJCV, 2021.
  * [6] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko.  End-to-end object detection with transformers.  In ECCV, 2020.
  * [7] Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, and Ming Liu.  Deep metric learning for open world semantic segmentation.  In Proceedings of the IEEE/CVF ICCV, 2021.
  * [8] Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, and Barbara Caputo.  Incremental learning in semantic segmentation from image labels.  In CVPR, 2022.
  * [9] Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, and Barbara Caputo.  Modeling the background for incremental learning in semantic segmentation.  In CVPR, 2020.
  * [10] Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Mathieu Salzmann, Pascal Fua, and Matthias Rottmann.  Segmentmeifyoucan: A benchmark for anomaly segmentation.  arXiv preprint arXiv:2104.14812, 2021.
  * [11] Robin Chan, Matthias Rottmann, and Hanno Gottschalk.  Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation.  In Proceedings of the IEEE/CVF CVPR, 2021.
  * [12] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam.  Encoder-decoder with atrous separable convolution for semantic image segmentation.  In ECCV, 2018.
  * [13] Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar.  Masked-attention mask transformer for universal image segmentation.  In Proceedings of the IEEE/CVF CVPR, 2022.
  * [14] Bowen Cheng, Alex Schwing, and Alexander Kirillov.  Per-pixel classification is not all you need for semantic segmentation.  Advances in NurIPS, 2021.
  * [15] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele.  The cityscapes dataset for semantic urban scene understanding.  In Proc. of the IEEE CVPR, 2016.
  * [16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei.  Imagenet: A large-scale hierarchical image database.  In IEEE CVPR, 2009.
  * [17] Giancarlo Di Biase, Hermann Blum, Roland Siegwart, and Cesar Cadena.  Pixel-wise anomaly detection in complex driving scenes.  In Proceedings of the IEEE/CVF CVPR, 2021.
  * [18] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun.  Carla: An open urban driving simulator.  In Conference on robot learning, 2017.
  * [19] Arthur Douillard, Yifu Chen, Arnaud Dapogny, and Matthieu Cord.  Plop: Learning without forgetting for continual semantic segmentation.  In CVPR, 2021.
  * [20] Xianzhi Du, Barret Zoph, Wei-Chih Hung, and Tsung-Yi Lin.  Simple training strategies and model scaling for object detection.  arXiv preprint arXiv:2107.00057, 2021.
  * [21] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman.  The pascal visual object classes (voc) challenge.  IJCV, 2010.
  * [22] Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, and Barbara Caputo.  Detecting anomalies in semantic segmentation with prototypes.  In Proceedings of the IEEE/CVF CVPR, 2021.
  * [23] Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Séverine Dubuisson, and Isabelle Bloch.  One versus all for deep neural network incertitude (ovnni) quantification.  arXiv preprint arXiv:2006.00954, 2020.
  * [24] Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Séverine Dubuisson, and Isabelle Bloch.  Tradi: Tracking deep neural network weight distributions.  In ECCV, 2020.
  * [25] Matej Grcić, Petra Bevandić, and Siniša Šegvić.  Dense open-set recognition with synthetic outliers generated by real nvp.  arXiv preprint arXiv:2011.11094, 2020.
  * [26] Matej Grcić, Petra Bevandić, and Siniša Šegvić.  Densehybrid: Hybrid anomaly detection for dense open-set recognition.  In ECCV, 2022.
  * [27] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.  Mask r-cnn.  In Proceedings of the IEEE ICCV, 2017.
  * [28] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In Proceedings of the IEEE CVPR, 2016.
  * [29] Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song.  Scaling out-of-distribution detection for real-world settings.  arXiv preprint arXiv:1911.11132, 2019.
  * [30] Dan Hendrycks and Kevin Gimpel.  A baseline for detecting misclassified and out-of-distribution examples in neural networks.  arXiv preprint arXiv:1610.02136, 2016.
  * [31] Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich.  Deep anomaly detection with outlier exposure.  arXiv preprint arXiv:1812.04606, 2018.
  * [32] Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, and Bohyung Han.  Exemplar-based open-set panoptic segmentation network.  In Proceedings of the IEEE/CVF CVPR, 2021.
  * [33] Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo.  Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation.  In Proceedings of the IEEE/CVF ICCV, 2021.
  * [34] Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollár.  Panoptic segmentation.  In Proceedings of the IEEE/CVF CVPR, 2019.
  * [35] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell.  Simple and scalable predictive uncertainty estimation using deep ensembles.  Advances in NurIPS, 2017.
  * [36] Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin.  A simple unified framework for detecting out-of-distribution samples and adversarial attacks.  Advances in NurIPS, 2018.
  * [37] Shiyu Liang, Yixuan Li, and Rayadurgam Srikant.  Enhancing the reliability of out-of-distribution image detection in neural networks.  arXiv preprint arXiv:1706.02690, 2017.
  * [38] Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid.  Refinenet: Multi-path refinement networks for high-resolution semantic segmentation.  In CVPR, 2017.
  * [39] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick.  Microsoft coco: Common objects in context.  In ECCV, 2014.
  * [40] Krzysztof Lis, Krishna Nakka, Pascal Fua, and Mathieu Salzmann.  Detecting the unexpected via image resynthesis.  In Proceedings of the IEEE/CVF ICCV, 2019.
  * [41] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li.  Energy-based out-of-distribution detection.  Advances in NurIPS, 2020.
  * [42] Jonathan Long, Evan Shelhamer, and Trevor Darrell.  Fully convolutional networks for semantic segmentation.  In CVPR, 2015.
  * [43] Ilya Loshchilov and Frank Hutter.  Decoupled weight decay regularization.  arXiv preprint arXiv:1711.05101, 2017.
  * [44] Umberto Michieli and Pietro Zanuttigh.  Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations.  In Proceedings of the IEEE/CVF CVPR, 2021.
  * [45] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi.  V-net: Fully convolutional neural networks for volumetric medical image segmentation.  In 3DV, 2016.
  * [46] Jishnu Mukhoti and Yarin Gal.  Evaluating bayesian deep learning methods for semantic segmentation.  arXiv preprint arXiv:1811.12709, 2018.
  * [47] Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder.  The mapillary vistas dataset for semantic understanding of street scenes.  In Proceedings of the IEEE ICCV, 2017.
  * [48] Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, and Rudolf Mester.  Lost and found: detecting small road hazards for self-driving vehicles.  In IEEE/RSJ IROS, 2016.
  * [49] Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, and Barbara Caputo.  Unmasking anomalies in road-scene segmentation.  arXiv preprint arXiv:2307.13316, 2023.
  * [50] Matthias Rottmann, Pascal Colling, Thomas Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, and Hanno Gottschalk.  Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities.  In IJCNN, 2020.
  * [51] Ruoqi Sun, Xinge Zhu, Chongruo Wu, Chen Huang, Jianping Shi, and Lizhuang Ma.  Not all areas are equal: Transfer learning for semantic segmentation via hierarchical region selection.  In 2019 IEEE/CVF CVPR, 2019.
  * [52] Yiyou Sun, Chuan Guo, and Yixuan Li.  React: Out-of-distribution detection with rectified activations.  Advances in NurIPS, 2021.
  * [53] Antonio Tavera, Fabio Cermelli, Carlo Masone, and Barbara Caputo.  Pixel-by-pixel cross-domain alignment for few-shot semantic segmentation.  In 2022 IEEE/CVF WACV, 2022.
  * [54] Yu Tian, Yuyuan Liu, Guansong Pang, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro.  Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes.  In ECCV, 2022.
  * [55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  Advances in NurIPS, 2017.
  * [56] Tomas Vojir, Tomáš Šipka, Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino, and Jiri Matas.  Road anomaly detection by partial image reconstruction with segmentation coupling.  In Proceedings of the IEEE/CVF ICCV, 2021.
  * [57] Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and Alan Yuille.  Synthesize then compare: Detecting failures and anomalies for semantic segmentation.  In ECCV, 2020.
  * [58] Yanchao Yang and Stefano Soatto.  Fda: Fourier domain adaptation for semantic segmentation.  In 2020 IEEE/CVF CVPR, 2020.
  * [59] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny.  Barlow twins: Self-supervised learning via redundancy reduction.  In ICML, 2021.
  * [60] Junyi Zhang, Ziliang Chen, Junying Huang, Liang Lin, and Dongyu Zhang.  Few-shot structured domain adaptation for virtual-to-real scene parsing.  In 2019 IEEE/CVF ICCVW, 2019.
  * [61] Zhenli Zhang, Xiangyu Zhang, Chao Peng, Xiangyang Xue, and Jian Sun.  Exfuse: Enhancing feature fusion for semantic segmentation.  In ECCV, 2018.
  * [62] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia.  Pyramid scene parsing network.  In CVPR, 2017.
  * [63] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba.  Scene parsing through ade20k dataset.  In Proceedings of the IEEE CVPR, 2017.
  * [64] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai.  Deformable detr: Deformable transformers for end-to-end object detection.  arXiv preprint arXiv:2010.04159, 2020.

|  Shyam Nandan Rai Shyam Nandan Rai is an ELLIS Ph.D. student in Computer and
Control Engineering at the Politecnico di Torino, funded by Italian National
PhD Program in Artificial Intelligence. He is a member of the Visual Learning
and Multimodal Applications Laboratory (VANDAL), supervised by Prof. Barbara
Caputo and Prof. Carlo Masone. He received his master’s degree in Computer
Science at IIIT Hyderabad, in 2020.
---|---
|  Fabio Cermelli Fabio Cermelli is a Ph.D. student in Computer and Control
Engineering at the Politecnico di Torino, funded by the Italian Insitute of
Technology (IIT). He received his master thesis in Software Engineering
(Computer Engineering) with honors at the Politecnico di Torino in 2018. He is
member of the Visual Learning and Multimodal Applications Laboratory (VANDAL),
supervised by Prof. Barbara Caputo. During his first year, he was a visiting
Ph.D. student in the Technologies of Vision Laboratory at FBK.
---|---
|  Barbara Caputo Barbara Caputo received the Ph.D. degree in computer science
from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 2005. From
2007 to 2013, she was a Senior Researcher with Idiap-EPFL (CH). Then, she
moved to Sapienza Rome University thanks to a MUR professorship, and joined
the Politecnico di Torino, in 2018. Since 2017, she has been a double
affiliation with the Italian Institute of Technology (IIT). She is currently a
Full Professor with the Politecnico of Torino, where she leads the Hub
AI@PoliTo. She is one of the 30 experts who contributed to write the Italian
Strategy on AI, and coordinator of the Italian National Ph.D. on AI and
Industry 4.0, sponsored by MUR. She is also an ERC Laureate and an ELLIS
Fellow. Since 2019, she serves on the ELLIS Board.
---|---
|  Carlo Masone (Member, IEEE) Carlo Masone is currently an Assistant
Professor at Politecnico di Torino. He received his B.S. degree and M.S.
degree in control engineering from the Sapienza University, Rome, Italy, in
2006 and 2010 respectively, and he received his Ph.D. degree in control
engineering from the University of Stuttgart in collaboration with the Max
Planck Institute for Biological Cybernetics (MPI-Kyb), Stuttgart, Germany, in
2014. From 2014 to 2017 he was a postdoctoral researcher at MPI-kyb, within
the Autonomous Robotics & Human-Machine Systems group. From 2017 to 2020 he
worked in industry on the development of self-driving cars. From 2020 to 2022
he was a senior researcher at the Visual and Multimodal Applied Learning, at
Politecnico di Torino.
---|---