2022

1]School of Computer Science, Wuhan University, Wuhan, China

2]Computer Vision Lab, ETH Zürich, Zürich, Switzerland

3]Vision for Autonomous Systems Group, MPI for Informatics, Saarbrücken,
Germany

# Deep Gradient Learning for Efficient Camouflaged Object Detection

Ge-Peng Ji  Deng-Ping Fan🖂 Yu-Cheng Chou  Dengxin Dai  Alexander Liniger  Luc
Van Gool  [  [  [

###### Abstract

This paper introduces DGNet, a novel deep framework that exploits object
gradient supervision for camouflaged object detection (COD). It decouples the
task into two connected branches, _i.e._ , a context and a texture encoder.
The essential connection is the gradient-induced transition, representing a
soft grouping between context and texture features. Benefiting from the simple
but efficient framework, DGNet outperforms existing state-of-the-art COD
models by a large margin. Notably, our efficient version, DGNet-S, runs in
real-time (80 fps) and achieves comparable results to the cutting-edge model
JCSOD-CVPR21 with only 6.82% parameters. Application results also show that
the proposed DGNet performs well in polyp segmentation, defect detection, and
transparent object segmentation tasks. Codes will be made available at
https://github.com/GewelsJI/DGNet.

###### keywords:

Camouflaged object detection, object gradient, soft grouping, efficient model,
image segmentation.

##  1 Introduction

Camouflaged object detection fan2020camouflaged ; fan2021concealed  (COD) aims
to segment objects with either artificial or natural patterns where objects
‘perfectly’ blend into the background to avoid being discovered
fan2021concealed . Several successful applications, such as medical image
analysis (_e.g._ , polyp fan2020pranet ; ji2021pnsnet ; ji2022vps  and lung
infection fan2020inf ; wu2021jcs ; liu2021covid  segmentation), video
understanding (_e.g._ , surveillance chen2022pedestrian  and autonomous
driving xue2018survey ) and recreational art feng2013facilitating ;
dean2017art , have shown COD’s scientific and practical value. ††🖂
Corresponding author (dengpfan@gmail.com). The major part of this work was
done while Ge-Peng Ji was an intern mentored by Deng-Ping Fan.

\begin{overpic}[width=433.62pt]{./Imgs/FeatureVis_v5-min}
\put(0.0,-4.0){\footnotesize Camouflaged Image}
\put(32.7,-4.0){\footnotesize(a) Object Boundary}
\put(66.8,-4.0){\footnotesize(b) Object Gradient} \end{overpic} Figure 1:
Feature visualization of learned texture. We observe that the proposed DGNet-S
under the object boundary supervision (a) contains diffused noises in the
background. By contrast, object gradient supervision (b) enforces the network
focus on the regions where the intensity changes dramatically.

Recent studies mei2021camouflaged ; lyu2021simultaneously ; jia2022segment ;
zhong2022detecting  present compelling results based on the supervision of the
whole object-level ground-truth mask. Later, various sophisticated techniques,
_e.g._ , boundary-based zhai2021mutual ; ji2021fast ; zhu2022can  and
uncertainty-guided yang2021uncertainty ; li2021uncertainty , were developed to
augment COD’s underlying representations. However, features learned from
boundary-supervised or uncertainty-based models usually respond to the sparse
edge of camouflage objects, thereby introducing noisy features, especially for
complex scenes (see Fig. 1-a). Besides, the boundaries of camouflaged objects
are always ‘indefinable’ or ‘fuzzy’; thus, they do not be pop-out from a quick
visual scanning. We notice that despite the object’s camouflage, there are
still some clues left, shown in the first column of Fig. 1 (white speckles).
Instead of extracting only boundary or uncertainty regions, we are interested
in how the network mines these ‘discriminative patterns’ inside the object.

From this perspective, we present our deep gradient network (DGNet) via the
explicit supervision of the object-level gradient map. The underlying
hypothesis is that some intensity changes inside the camouflaged objects. To
ease the learning task, we decouple the DGNet into two connected branches,
_i.e._ , a context and a texture encoder. The former can be viewed as a
contextual semantics learner, while the latter acts as a structural texture
extractor. In this way, we can alleviate the feature ambiguity between the
high-level and low-level features extracted from the individual branch. To
sufficiently aggregate the above two discriminative features generated by the
two branches, we further design a gradient-induced transition (GIT) module
that collaboratively ensembles the multi-source feature space at different
group scales (_i.e._ , soft grouping). Fig. 1-b shows that our DGNet can
detect texture patterns while suppressing the background noise by an
intensity-sensitive strategy focusing on the intra-region of a camouflaged
object.

Extensive experiments on three challenging COD benchmarks illustrate that the
proposed DGNet achieves state-of-the-art (SOTA) performance without
introducing any complicated structures. Furthermore, we implement an efficient
version, DGNet-S, with 8.3M parameters, which achieves the fastest inference
speed (80 fps) among COD-related baselines. Notably, it only has 6.82%
parameters compared to the cutting-edge model JCSOD-CVPR21 li2021uncertainty
while achieving comparable performance. These results show that DGNet
significantly narrows the gap between scientific research and practical
application. Three downstream applications (see Sec. 5) of our DGNet also
support this conclusion. The major contributions of this paper are summarized
as follows:

  * $\bullet$

We introduce a novel deep gradient-based framework, dubbed DGNet, for
addressing the camouflaged object detection task.

  * $\bullet$

We propose a gradient-induced transition to automatically group features from
the context and texture branches according to the soft grouping strategy.

  * $\bullet$

We present three applications and achieve good performance, including polyp
segmentation, defect detection, and transparent object segmentation.

##  2 Prior Works

Traditional methods detect camouflaged objects via extracting various hand-
crafted features between the camouflaged areas and their backgrounds, which
calculate the 3D convexity pan2011study , co-occurrence matrix
sengottuvelan2008performance , expectation-maximization statistics
liu2012foreground , optical flow hou2011detection , and Gaussian mixture model
gallego2014foreground . Those methods work well for simple backgrounds, while
the performance degrades drastically for complex backgrounds.

CNN-based approaches could be generally categorized into three strategies: a)
Attention-based strategy: Sun et al. sun2021Context ; chen2022camouflaged
introduce a network with an attention-induced cross-level fusion module to
integrate multi-scale features and a dual-branch global context module to mine
multi-scale contextual information. To mimic the detection process of
predators, Mei et al. mei2021camouflaged  develop PFNet, which contains a
positioning and focusing module to conduct the identification. ren2021deep ;
ji2021fast  propose delicate structures such as covariance matrices of feature
and multivariate calibration components to improve the robustness of the
network. Kajiura et al. Kajiura2021improving  improve the detection accuracy
via exploring the uncertainties of pseudo-edge and pseudo-map labels. Zhuge et
al. zgmc2022cubenet  propose a cube-alike architecture for COD, which
accompanies attention fusion and X-shaped connection to integrate multiple-
layer features sufficiently. b) Two-stage strategy: Search and identification
strategy fan2020camouflaged  is an early practice to model the COD task. In
fan2021concealed , the neighbour connection decoder and group-reversal
attention are introduced in SINet fan2020camouflaged  to boost the performance
further. c) Joint-learning strategy: ANet le2019anabranch  is an early attempt
to utilize the classification and segmentation scheme for COD. LSR
lyu2021simultaneously  and JCSOD li2021uncertainty  have recently renewed the
joint-learning framework by introducing camouflaged ranking or learning from
salient objects to camouflaged objects. ZoomNet pang2022zoom  is a mixed-scale
triplet network that employs the zoom strategy to learn the discriminative
camouflaged semantics.

Transformer-based & Graph-based models are two recent technology trends.
Recently, Mao et al. mao2021transformer  introduce the concept of difficulty-
aware learning based on the Transformer for both camouflaged and salient
object detection. UGTR yang2021uncertainty  explicitly utilizes the
probabilistic representational model to learn the uncertainties of the
camouflaged object under the Transformer framework. In addition, Cheng et al.
cheng2022implicit  are the first to collect a video dataset for COD and
utilise the Transformer-based framework to exploit short-term dynamics and
long-term temporal consistency to detect dynamic camouflaged objects. Later,
Zhai et al. zhai2021mutual  design the mutual graph learning model, which
decouples one input into different features for roughly locating the target
and accurately capturing its boundary.

\begin{overpic}[width=433.62pt]{./Imgs/Gradient_Comp_v4-min}
\put(0.5,22.5){\footnotesize\color[rgb]{1,1,1} Camouflaged Image}
\put(37.0,22.5){\footnotesize\color[rgb]{1,1,1} (a) DGNet-Grad}
\put(55.0,38.0){\footnotesize\color[rgb]{1,1,1} {{(Ours)}}}
\put(74.0,22.5){\footnotesize\color[rgb]{1,1,1} (b) Feature}
\put(88.5,38.0){\footnotesize\color[rgb]{1,1,1} {{(Ours)}}}
\put(6.0,1.5){\footnotesize\color[rgb]{1,1,1} Ground-truth}
\put(38.0,1.5){\footnotesize\color[rgb]{1,1,1} (c) TINet-Text}
\put(74.0,1.5){\footnotesize\color[rgb]{1,1,1} (d) Feature} \end{overpic}
Figure 2: Compared to the texture label proposed in TINet zhu2021inferring ,
our object gradient label (a) keeps more geometric cues inside the camouflaged
object. DGNet under the supervision of texture label (c) fails to infer
attentive regions (d) since the imbalanced distribution of sparse pixels
(_e.g._ , thin object boundaries). Notably, such improvement exerts our DGNet
more robust with the reliable auxiliaries, _e.g._ , feature in (b).

Remarks. By contrast, our work excavates the texture information via learning
the object-level gradient rather than using boundary-aware or uncertainty-
aware modelling. The biologically inspired idea behind this is that the
abundant gradient cues inside the camouflaged object deserve to be explored,
while the sparse boundary cues are insufficient to achieve this. As shown in
Fig. 2, we also note that the recent work zhu2021inferring  tries to utilize
the texture cues while they discard excessive object gradient cues due to
different threshold settings of the Canny detector. In short, this paper aims
to design an elegant framework towards efficient COD with more concise ideas
(_i.e._ , object gradient learning). More experimental validations are
discussed in Sec. 4.3.

##  3 Deep Gradient Network

As discussed in lin2017feature , the low-level and high-level features occupy
an equal role in the scene understanding. As suggested by ke2022modnet , it is
not encouraged to encode them simultaneously. As shown in Fig. 3, we propose
to model the camouflaged representations with two separate encoders, a context
and a texture encoder.

###  3.1 Context Encoder

For a camouflaged input image $\mathcal{I}\in\mathbb{R}^{3\times H\times W}$,
we use the widely used EfficientNet tan2019efficientnet  as the context
encoder to obtain the pyramid features $\\{\mathbf{X}_{i}\\}_{i=1}^{5}$.

Dimensional Reduction. Inspired by fan2020pranet , we adopt the following two
steps to ensure efficient element-wise operations between different levels in
the decoding stage: a) we only pick out the top-three features (_i.e._ , when
$i=3,4,5$), which retain the affluent semantics of the visual scene. b) we
further utilize two stacked ConvBR111In this paper, ConvBR denotes the
standard convolutional layer followed by a BN ioffe2015batch  layer and a ReLU
glorot2011deep  layer. layers with $C_{i}\times 3\times 3$ filters to reduce
the dimension of each candidate feature to $C_{i}$, contributing to easing the
computational burden of subsequent operations. The final outputs are three
context features $\\{\mathbf{X}_{i}^{R}\\}_{i=3}^{5}\in\mathbb{R}^{C_{i}\times
H_{i}\times W_{i}}$, where $C_{i}$, $H_{i}=\frac{H}{2^{i}}$, and
$W_{i}=\frac{W}{2^{i}}$ denote the channel, height, and width of the feature
maps.

\begin{overpic}[width=364.2392pt]{./Imgs/DGNet-Framework-V1-min} \end{overpic}
Figure 3: Overall pipeline of the proposed DGNet. It consists of two connected
learning branches, _i.e._ , context encoder (Sec. 3.1) and texture encoder
(Sec. 3.2). Then, we introduce a gradient-induced transition (GIT) (Sec. 3.3)
to collaboratively aggregate the feature that is derived from the above two
encoders. Finally, a neighbor connected decoder (NCD) fan2021concealed  is
adopted to generate the prediction $\mathbf{P}^{C}$ (Sec. 3.4).

###  3.2 Texture Encoder

We also introduce a tailored texture branch supervised by the object-level
gradient map, compensating for the pattern degradation caused by the top-three
context features’ weak representation of geometric textures.

Object Gradient Generation. An image gradient describes the directional change
in an image’s intensity or color between adjacent positions, which is widely
applied for edge detection su2021pixel  and super-resolution ma2020structure .
The right part of Fig. 3 presents four widely used types of supervision
labels. The object boundary (c) and image gradient (e) can be directly
generated by calculating the gradient of the object-level ground-truth
$\mathbf{Z}^{C}$ (b) and raw image (a), respectively. However, the raw image
gradient map (e), which contains irrelevant background noises, may mislead the
optimization process when serving as the supervision signal for texture
learning. To address this problem, we introduce a novel camouflage learning
paradigm that uses the object-level gradient map $\mathbf{Z}^{G}$ (d) as
supervision, which holds both the gradient cues of the object’s boundaries and
interior regions. This process could be formulated as:

$\mathbf{Z}^{G}=\mathcal{F}_{E}(\mathcal{I}(x,y))\circledast\mathbf{Z}^{C},$
(1)

where $\mathcal{F}_{E}$ represents the standard Canny edge detector
canny1986computational  for input $\mathcal{I}$ with discrete pixel
coordinates $(x,y)$. $\circledast$ means the element-wise multiplication.

Table 1: Details of the tailored texture encoder. $k$: kernel size, $c$: output channels, $s$: stride, and $p$: zero-padding. Here, we set the channel $C_{g}=32$ as default setting. Layer | Input Size | Output Size | Component | $k$ | $c$ | $s$ | $p$
---|---|---|---|---|---|---|---
$\\#01$ | $3\times H\times W$ | $64\times\frac{H}{2}\times\frac{W}{2}$ | ConvBR | 7 | 64 | 2 | 3
$\\#02$ | $64\times\frac{H}{2}\times\frac{W}{2}$ | $64\times\frac{H}{4}\times\frac{W}{4}$ | ConvBR | 3 | 64 | 2 | 1
$\\#03$ | $64\times\frac{H}{4}\times\frac{W}{4}$ | $C_{g}\times\frac{H}{8}\times\frac{W}{8}$ | ConvBR | 3 | $C_{g}$ | 2 | 1
$\\#04$ | $C_{g}\times\frac{H}{8}\times\frac{W}{8}$ | $1\times\frac{H}{8}\times\frac{W}{8}$ | ConvBR | 1 | 1 | 1 | 0

Texture Encoder. Because low-level features with a high resolution will
introduce a computational burden, we design a tailored lightweight encoder
instead of utilizing an out-of-box backbone. We obtain the texture feature
$\mathbf{X}^{G}\in\mathbb{R}^{C_{g}\times H_{g}\times W_{g}}$ from
layer$\\#03$ (see Table 1). However, we supervise the following layer$\\#04$
with the object-level gradient $\mathbf{Z}^{G}$. We keep the texture feature
with a larger resolution (_i.e._ , $H_{g}=\frac{H}{8}$ and
$W_{g}=\frac{W}{8}$) since the features with smaller resolution would discard
most geometric details.

###  3.3 Gradient-Induced Transition

The latent correlation between context and texture features offers great
potential for adaptive fusion rather than adopting naive fusion strategies
(_e.g._ , concatenation and addition operations). Here, we design a flexible
plug-and-play gradient-induced transition (GIT) module (see Fig. 4), which
views the texture feature as the auxiliaries in the multi-source aggregation
from a group-wise perspective. Specifically, it comprises the following three
steps.

\begin{overpic}[width=411.93767pt]{./Imgs/GIT-V5-min} \end{overpic} Figure 4:
Illustration of the proposed gradient-induced transition (GIT).

Gradient-Induced Group Learning. Inspired by fan2021concealed , we first adopt
the gradient-induced group learning strategy, which splits three context
features $\\{\mathbf{X}^{R}_{i}\\}_{i=3}^{5}$ and a texture feature
$\mathbf{X}^{G}$ into fixed groups along the channel dimension. This strategy
can be formulated for each $\mathbf{X}^{R}_{i}$ and $\mathbf{X}^{G}$ pair:

$\begin{split}\\{\mathbf{X}_{i,m}^{R}\\}_{m=1}^{M}\in\mathbb{R}^{K_{i}\times
H_{i}\times W_{i}}\leftarrow\mathbf{X}^{R}_{i}\in\mathbb{R}^{C_{i}\times
H_{i}\times W_{i}},\\\
\\{\mathbf{X}_{m}^{G}\\}_{m=1}^{M}\in\mathbb{R}^{K_{g}\times H_{g}\times
W_{g}}\leftarrow\mathbf{X}^{G}\in\mathbb{R}^{C_{g}\times H_{g}\times
W_{g}},\end{split}$ (2)

where $\leftarrow$ is the feature grouping operation. $K_{i}=C_{i}/M$ and
$K_{g}=C_{g}/M$ denote the channel number of each feature group, and $M$ is
the corresponding number of groups. Then, we periodically arrange groups of
context features $\mathbf{X}_{i,m}^{R}$ and texture features
$\mathbf{X}^{G}_{m}$, which generates the regrouped feature $\mathbf{Q}_{i}$
via:

$\mathbf{Q}_{i}\in\mathbb{R}^{(C_{i}+C_{g})\times H_{i}\times
W_{i}}=\langle\mathbf{Y}^{1}_{i}\mathchar 24635\relax\;\dots\mathchar
24635\relax\;\mathbf{Y}^{m}_{i}\mathchar 24635\relax\;\dots\mathchar
24635\relax\;\mathbf{Y}^{M}_{i}\rangle,$ (3)

where $\langle\cdot\mathchar 24635\relax\;\cdot\rangle$ means the channel-wise
feature concatenation. Here, the m-th sub-component $\mathbf{Y}^{m}_{i}$ is
derived from:

$\mathbf{Y}^{m}_{i}\in\mathbb{R}^{(K_{i}+K_{g})\times H_{i}\times
W_{i}}=\langle\mathcal{F}_{\downarrow}({\mathbf{X}^{G}_{m}})\mathchar
24635\relax\;\mathbf{X}_{i,m}^{R}\rangle,$ (4)

where the down-sampling operation $\mathcal{F}_{\downarrow}(\cdot)$ ensures
the spatial resolution of $\mathbf{X}_{m}^{G}$ matches $\mathbf{X}_{i,m}^{R}$.

Soft Grouping Strategy. The naive feature fusion strategies may ignore the
correlation or distinctiveness between context and texture representations due
to lacking further multi-source interactions. Inspired by the parallel design
introduced in chen2017deeplab  for capturing objects at multiple scales, we
propose using a soft grouping strategy to provide parallel nonlinear
projections at multiple fine-grained sub-spaces, which enables the network to
probe multi-source representations jointly. Specifically, we set three
parallel sub-branches (_i.e._ , $\\{N_{1},N_{2},N_{3}\\}$ as in the gray
region of Fig. 4) for the soft grouping in our experiment. Here, for
simplifying illustration, we take the $N$-th sub-branch as an example via
omitting the subscript, which could be formulated as:

$\mathbf{A}_{i}^{N}=\langle\mathcal{F}^{1}_{N}(\mathbf{Q}_{i}^{1})\mathchar
24635\relax\;\dots\mathchar
24635\relax\;\mathcal{F}^{n}_{N}(\mathbf{Q}_{i}^{n})\dots\mathcal{F}^{N}_{N}(\mathbf{Q}_{i}^{N})\rangle,$
(5)

where $\mathcal{F}^{n}_{N}(\mathbf{Q}_{i}^{n})\in\mathbb{R}^{(C_{i}/N)\times
H_{i}\times W_{i}}=f_{n}(\mathbf{Q}_{i}^{n},\omega_{n})$ intentionally
introduces soft non-linearity at each multi-source sub-space. The projection
function $f_{n}$ is implemented by a convolutional layer with $C_{i}$ filters
of shape of $\frac{(C_{i}+C_{g})}{N}\times 1\times 1$, which is parameterized
by learnable weights $\omega_{n}$. Here, $\mathbf{Q}_{i}^{n}$ is the n-th
subset of the regrouped feature $\mathbf{Q}_{i}$ that is divided into $N$
groups.

Parallel Residual Learning. We further introduce residual learning he2016deep
in a parallel manner at different group-aware scales. Consequently, we can
define the GIT function $\mathcal{T}_{i}(\cdot,\cdot)$ (see the red block in
Fig. 3) as:

$\mathbf{Z}_{i}^{T}=\mathcal{T}_{i}(\mathbf{X}^{R}_{i},\mathbf{X}^{G})=\mathbf{X}_{i}^{R}\oplus\sum_{N}\mathbf{A}_{i}^{N},$
(6)

where $N\in\\{N_{1},N_{2},N_{3}\\}$ denotes a set of scaling factors for
different groups, which will be discussed in Sec. 4.3. $\oplus$ means the
element-wise addition, and $\sum$ denotes a sum of multiple terms. The final
output is $\\{\mathbf{Z}_{i}^{T}\\}_{i=3}^{5}\in\mathbb{R}^{C_{i}\times
H_{i}\times W_{i}}$.

###  3.4 Learning Details

Decoder. Given the context features $\\{\mathbf{X}_{i}^{R}\\}_{i=3}^{5}$, we
firstly apply the GIT function $\mathcal{T}_{i}(\cdot,\cdot)$ (see Equ.(6)) to
get the output features $\\{\mathbf{Z}_{i}^{T}\\}_{i=3}^{5}$. To exploit the
above gradient-induced features in $\mathbf{Z}^{T}_{i}$ more efficiently, we
utilize the neighbour connection decoder (NCD) fan2021concealed  to generate
the final prediction, enabling feature propagation from high to low levels.
Thus, the final prediction $\mathbf{P}^{C}$ can be derived from
$\mathbf{P}^{C}\in\mathbb{R}^{1\times H\times
W}=\text{NCD}(\mathbf{Z}_{3}^{T},\mathbf{Z}_{4}^{T},\mathbf{Z}_{5}^{T})$.

Loss Function. The overall optimization objective is defined as:

$\mathcal{L}=\mathcal{L}_{C}(\mathbf{P}^{C},\mathbf{Z}^{C})+\mathcal{L}_{G}(\mathbf{P}^{G},\mathbf{Z}^{G}),$
(7)

where $\mathcal{L}_{C}$ and $\mathcal{L}_{G}$ represent the segmentation and
object gradient loss functions, respectively. For the former, it is formulated
as $\mathcal{L}_{C}=\mathcal{L}_{IoU}^{w}+\mathcal{L}_{BCE}^{w}$, where
$\mathcal{L}_{IoU}^{w}$ and $\mathcal{L}_{BCE}^{w}$ represent the weighted
intersection-over-union (IoU) loss and the weighted binary cross-entropy (BCE)
loss, respectively. They assign the adaptive weight for each pixel according
to its difficulty in focusing on the global structure and paying more
attention to the hard pixels. The definitions of these losses are the same as
in wei2020f3net ; fan2020camouflaged ; fan2021concealed  and their
effectiveness has been proven in binary segmentation. For the latter, we
employ the standard mean squared error loss function.

Table 2: Hyper-parameter settings of the proposed DGNet-S and DGNet. Model | Backbone | $C_{i}$ | $C_{g}$ | $M$ | $\\{N_{1},N_{2},N_{3}\\}$
---|---|---|---|---|---
DGNet-S | EfficientNet-B1 | 32 | 32 | 8 | $\\{8,16,32\\}$
DGNet | EfficientNet-B4 | 64 | 32 | 8 | $\\{4,8,16\\}$

Training Settings. The proposed DGNet is implemented in the PyTorch
paszke2019pytorch /Jittor hu2020jittor  toolbox and trained/inferred on a
single NVIDIA RTX TITAN GPU. The model parameters are initialized with the
strategy of he2015delving , and we initialize the backbone with the model
weights pre-trained on ImageNet krizhevsky2012imagenet  to prevent over-
fitting. We discard the last stage of Conv1$\times$1, pooling, and fully
connected layers in the EfficientNet tan2019efficientnet  backbone and extract
the features from the top-three lateral outputs, including stage-4
($\mathbf{X}_{3}$), stage-6 ($\mathbf{X}_{4}$), and stage-8
($\mathbf{X}_{5}$). Considering the performance-efficiency trade-off, we
instantiate two variants to adapt the specific requirement under various
computational overheads (refer to Table 2).

We train our model in an end-to-end manner, using Adam kingma2015adam . The
cosine annealing part of the SGDR strategy loshchilov2017sgdr  is used to
adjust the learning rate, where the minimum/maximum learning rate and the
maximum adjusted iteration are set to $10^{-5}$/$10^{-4}$ and 20,
respectively. The batch size is set to 12, and the maximum training epoch is
100. During training, we resize each image to 352$\times$352 and feed it into
DGNet with four data augmentation techniques: color enhancement, random
flipping, random cropping, and random rotation. Finally, our DGNet and DGNet-S
take 8.8 and 7.9 hours to reach the network convergence.

Testing Settings. Once the network is well-trained, we resize the input images
to 352$\times$352 and test our DGNet-S and DGNet on three unseen test
datasets. We take the final output $\mathbf{P}^{C}$ as the prediction map
without any heuristic post-processing techniques, such as DenseCRF
krahenbuhl2011efficient .

##  4 Experiments

###  4.1 Benchmarking

Datasets. There are three popular datasets in the COD field: a) CAMO
le2019anabranch  has 1,250 camouflaged images and is divided into CAMO-Tr
(1,000 samples) and CAMO-Te (250 samples). b) COD10K fan2021concealed  is the
largest COD dataset till now, consisting of COD10K-Tr (3,040 images) and
COD10K-Te (2,026 images). It is downloaded from multiple free photography
websites, covering 5 super-classes and 69 sub-classes. c) NC4K-Te
lyu2021simultaneously , as the largest testing dataset, includes 4,121
samples, which are used to evaluate the models’ generalization ability.
Following the protocol of fan2021concealed , we train our model on the hybrid
dataset (_i.e._ , COD10K-Tr \+ CAMO-Tr) with 4,040 samples and evaluate our
method on above three benchmarks (see Table 3).

Metrics. Following fan2021concealed , we use five commonly used metrics for
the evaluation: structure measure ($\mathcal{S}_{\alpha}$) fan2017structure ,
enhanced-alignment measure ($E_{\phi}$) Fan2018Enhanced ; fan2021cognitive ,
F-measure ($F_{\beta}$) borji2015salient ; zhuge2021salient , weighted
F-measure ($F_{\beta}^{w}$) margolin2014evaluate , and mean absolute error
($\mathcal{M}$). Besides, the precision-recall (PR) curves borji2015salient
are obtained by varying different thresholds from $[0,255]$. Similar to this
thresholding strategy, F-measure and E-measure curves are also reported.
Moreover, we adopt three criteria to measure the model’s complexity222The
model’s parameter and MACs are measured by the toolbox:
https://github.com/sovrasov/flops-counter.pytorch. and efficiency: the number
of model parameters, measured in Millions (M), the number of multiply-
accumulate (MACs) operations, measured in Giga (G), and inference speed
measured in frames per second (fps).

Table 3: Quantitative results in terms of full metrics for cutting-edge competitors, including 8 SOD-related and 12 COD-related, on three COD-related test datasets. @$\mathcal{R}$ is the ranking of the current metric, and Mean@$\mathcal{R}$ indicates the mean ranking of all metrics. Note that $E_{\phi}^{mx}$/$F_{\beta}^{mx}$, $E_{\phi}^{mn}$/$F_{\beta}^{mn}$, and $E_{\phi}^{ad}$/$F_{\beta}^{ad}$ denote the maximum, mean, and adaptive value of E-measure/F-measure, respectively. $\uparrow$/$\downarrow$ denotes that the higher/lower the score, the better. All the benchmark results are available at https://anu365-my.sharepoint.com/:u:/g/personal/u7248002_anu_edu_au/EXLiBgp9nGNApBw9im2xznsBJ_ryGEW7hkJlL92gNaRAAg?e=XIUBnq. |  |  | SOD-related Models
---
| COD-related Models
---
|
|  |  EGNet |  SCRN |  CPD |  CSNet-R |  F3Net |  UCNet |  ITSD |  MINet |  SINet |  PraNet |  BAS |  C2FNet |  TINet |  UGTR |  PFNet |  S-MGL |  R-MGL |  LSR |  JCSOD |  SINetV2 |  DGNet-S |  DGNet
| Metric | zhao2019EGNet  | wu2019stacked  | wu2019cascaded  | gao2020highly  | wei2020f3net  | zhang2020UCNet  | zhou2020interactive  | pang2020multi  | fan2020camouflaged  | fan2020pranet  | qin2021boundary  | sun2021Context  | zhu2021inferring  | yang2021uncertainty  | mei2021camouflaged  | zhai2021mutual  | zhai2021mutual  | lyu2021simultaneously  | li2021uncertainty  | fan2021concealed  |  | Ours
---
NC4K-Te lyu2021simultaneously  | $\mathcal{S}_{\alpha}\uparrow$ | .777 | .830 | .788 | .750 | .780 | .811 | .811 | .805 | .808 | .822 | .817 | .838 | .829 | .839 | .829 | .829 | .833 | .840 | .842 | .847 | .845 | .857
@$\mathcal{R}$ | 21 | 9 | 19 | 22 | 20 | 16 | 15 | 18 | 17 | 13 | 14 | 7 | 12 | 6 | 11 | 10 | 8 | 5 | 4 | 2 | 3 | 1
$E_{\phi}^{mx}\uparrow$ | .864 | .897 | .865 | .793 | .848 | .886 | .883 | .881 | .883 | .888 | .872 | .904 | .890 | .899 | .898 | .893 | .893 | .907 | .907 | .914 | .913 | .922
@$\mathcal{R}$ | 20 | 9 | 19 | 22 | 21 | 14 | 15 | 17 | 16 | 13 | 18 | 6 | 12 | 7 | 8 | 11 | 10 | 5 | 4 | 2 | 3 | 1
$E_{\phi}^{mn}\uparrow$ | .841 | .854 | .804 | .773 | .824 | .871 | .845 | .846 | .871 | .876 | .859 | .897 | .879 | .874 | .888 | .863 | .867 | .895 | .898 | .903 | .902 | .911
@$\mathcal{R}$ | 19 | 16 | 21 | 22 | 20 | 11 | 18 | 17 | 12 | 9 | 15 | 5 | 8 | 10 | 7 | 14 | 13 | 6 | 4 | 2 | 3 | 1
$E_{\phi}^{ad}\uparrow$ | .826 | .864 | .842 | .812 | .853 | .883 | .855 | .876 | .882 | .871 | .868 | .898 | .880 | .886 | .892 | .884 | .889 | .902 | .903 | .898 | .899 | .907
@$\mathcal{R}$ | 21 | 17 | 20 | 22 | 19 | 11 | 18 | 14 | 12 | 15 | 16 | 6 | 13 | 9 | 7 | 10 | 8 | 3 | 2 | 5 | 4 | 1
$F_{\beta}^{w}\uparrow$ | .639 | .698 | .632 | .603 | .656 | .729 | .680 | .705 | .723 | .724 | .732 | .762 | .734 | .747 | .745 | .731 | .740 | .766 | .771 | .770 | .764 | .784
@$\mathcal{R}$ | 20 | 17 | 21 | 22 | 19 | 13 | 18 | 16 | 15 | 14 | 11 | 6 | 10 | 7 | 8 | 12 | 9 | 4 | 2 | 3 | 5 | 1
$F_{\beta}^{mx}\uparrow$ | .731 | .793 | .738 | .669 | .719 | .782 | .762 | .768 | .775 | .786 | .782 | .810 | .793 | .807 | .799 | .797 | .800 | .815 | .816 | .823 | .819 | .833
@$\mathcal{R}$ | 20 | 11 | 19 | 22 | 21 | 15 | 18 | 17 | 16 | 13 | 14 | 6 | 12 | 7 | 9 | 10 | 8 | 5 | 4 | 2 | 3 | 1
$F_{\beta}^{mn}\uparrow$ | .696 | .757 | .695 | .655 | .705 | .775 | .729 | .753 | .769 | .762 | .772 | .795 | .773 | .787 | .784 | .777 | .782 | .804 | .806 | .805 | .799 | .814
@$\mathcal{R}$ | 20 | 16 | 21 | 22 | 19 | 11 | 18 | 17 | 14 | 15 | 13 | 6 | 12 | 7 | 8 | 10 | 9 | 4 | 2 | 3 | 5 | 1
$F_{\beta}^{ad}\uparrow$ | .671 | .744 | .709 | .672 | .710 | .776 | .717 | .763 | .768 | .753 | .767 | .788 | .766 | .779 | .779 | .771 | .778 | .802 | .803 | .792 | .789 | .803
@$\mathcal{R}$ | 22 | 17 | 20 | 21 | 19 | 10 | 18 | 15 | 12 | 16 | 13 | 6 | 14 | 8 | 7 | 11 | 9 | 3 | 2 | 4 | 5 | 1
$\mathcal{M}\downarrow$ | .075 | .059 | .074 | .088 | .070 | .055 | .064 | .060 | .058 | .059 | .058 | .049 | .055 | .052 | .053 | .055 | .052 | .048 | .047 | .048 | .047 | .042
@$\mathcal{R}$ | 21 | 15 | 20 | 22 | 19 | 10 | 18 | 17 | 14 | 16 | 13 | 6 | 12 | 8 | 9 | 11 | 7 | 5 | 2 | 4 | 3 | 1
|  Mean@$\mathcal{R}$ | 21 | 15 | 20 | 22 | 19 | 12 | 18 | 17 | 16 | 13 | 14 | 6 | 11 | 7 | 8 | 10 | 9 | 5 | 2 | 3 | 4 | 1
CAMO-Te le2019anabranch  | $\mathcal{S}_{\alpha}\uparrow$ | .732 | .779 | .726 | .771 | .711 | .739 | .750 | .737 | .745 | .769 | .749 | .796 | .781 | .785 | .782 | .772 | .775 | .787 | .800 | .820 | .826 | .839
@$\mathcal{R}$ | 20 | 10 | 21 | 13 | 22 | 18 | 15 | 19 | 17 | 14 | 16 | 5 | 9 | 7 | 8 | 12 | 11 | 6 | 4 | 3 | 2 | 1
$E_{\phi}^{mx}\uparrow$ | .820 | .850 | .801 | .849 | .780 | .820 | .830 | .818 | .829 | .837 | .808 | .864 | .848 | .854 | .855 | .842 | .842 | .854 | .873 | .895 | .907 | .915
@$\mathcal{R}$ | 17 | 9 | 21 | 10 | 22 | 18 | 15 | 19 | 16 | 14 | 20 | 5 | 11 | 8 | 6 | 13 | 12 | 7 | 4 | 3 | 2 | 1
$E_{\phi}^{mn}\uparrow$ | .800 | .797 | .723 | .795 | .741 | .787 | .780 | .767 | .804 | .824 | .796 | .854 | .836 | .823 | .842 | .807 | .812 | .838 | .859 | .882 | .893 | .901
@$\mathcal{R}$ | 14 | 15 | 22 | 17 | 21 | 18 | 19 | 20 | 13 | 9 | 16 | 5 | 8 | 10 | 6 | 12 | 11 | 7 | 4 | 3 | 2 | 1
$E_{\phi}^{ad}\uparrow$ | .811 | .848 | .810 | .847 | .802 | .811 | .830 | .826 | .825 | .833 | .806 | .864 | .845 | .859 | .852 | .850 | .847 | .855 | .865 | .875 | .892 | .901
@$\mathcal{R}$ | 18 | 10 | 20 | 12 | 22 | 19 | 15 | 16 | 17 | 14 | 21 | 5 | 13 | 6 | 8 | 9 | 11 | 7 | 4 | 3 | 2 | 1
$F_{\beta}^{w}\uparrow$ | .604 | .643 | .553 | .642 | .564 | .640 | .610 | .613 | .644 | .663 | .646 | .719 | .678 | .686 | .695 | .664 | .673 | .696 | .728 | .743 | .754 | .769
@$\mathcal{R}$ | 20 | 15 | 22 | 16 | 21 | 17 | 19 | 18 | 14 | 12 | 13 | 5 | 9 | 8 | 7 | 11 | 10 | 6 | 4 | 3 | 2 | 1
$F_{\beta}^{mx}\uparrow$ | .688 | .738 | .667 | .740 | .630 | .708 | .694 | .683 | .708 | .728 | .703 | .771 | .745 | .754 | .758 | .739 | .740 | .753 | .779 | .801 | .810 | .822
@$\mathcal{R}$ | 19 | 13 | 21 | 10 | 22 | 15 | 18 | 20 | 16 | 14 | 17 | 5 | 9 | 7 | 6 | 12 | 11 | 8 | 4 | 3 | 2 | 1
$F_{\beta}^{mn}\uparrow$ | .670 | .705 | .614 | .705 | .616 | .700 | .663 | .667 | .702 | .710 | .692 | .762 | .728 | .738 | .746 | .721 | .726 | .744 | .772 | .782 | .792 | .806
@$\mathcal{R}$ | 18 | 13 | 22 | 14 | 21 | 16 | 20 | 19 | 15 | 12 | 17 | 5 | 9 | 8 | 6 | 11 | 10 | 7 | 4 | 3 | 2 | 1
$F_{\beta}^{ad}\uparrow$ | .667 | .733 | .678 | .730 | .661 | .716 | .692 | .704 | .712 | .715 | .696 | .764 | .729 | .749 | .751 | .733 | .738 | .756 | .779 | .779 | .786 | .804
@$\mathcal{R}$ | 21 | 10 | 20 | 12 | 22 | 14 | 19 | 17 | 16 | 15 | 18 | 5 | 13 | 8 | 7 | 11 | 9 | 6 | 4 | 3 | 2 | 1
$\mathcal{M}\downarrow$ | .109 | .090 | .114 | .092 | .109 | .094 | .102 | .096 | .092 | .094 | .096 | .080 | .087 | .086 | .085 | .089 | .088 | .080 | .073 | .070 | .063 | .057
@$\mathcal{R}$ | 21 | 12 | 22 | 13 | 20 | 15 | 19 | 18 | 14 | 16 | 17 | 6 | 9 | 8 | 7 | 11 | 10 | 5 | 4 | 3 | 2 | 1
|  Mean@$\mathcal{R}$ | 20 | 12 | 21 | 13 | 22 | 16 | 18 | 19 | 15 | 14 | 17 | 5 | 9 | 8 | 7 | 11 | 10 | 6 | 4 | 3 | 2 | 1
COD10K-Te fan2021concealed  | $\mathcal{S}_{\alpha}\uparrow$ | .736 | .789 | .748 | .778 | .739 | .776 | .767 | .769 | .776 | .789 | .802 | .813 | .793 | .818 | .800 | .811 | .814 | .804 | .809 | .815 | .810 | .822
@$\mathcal{R}$ | 22 | 13 | 20 | 15 | 21 | 17 | 19 | 18 | 16 | 14 | 10 | 5 | 12 | 2 | 11 | 6 | 4 | 9 | 8 | 3 | 7 | 1
$E_{\phi}^{mx}\uparrow$ | .855 | .880 | .842 | .871 | .819 | .867 | .861 | .864 | .874 | .879 | .870 | .900 | .878 | .891 | .890 | .890 | .890 | .892 | .891 | .906 | .905 | .911
@$\mathcal{R}$ | 20 | 11 | 21 | 15 | 22 | 17 | 19 | 18 | 14 | 12 | 16 | 4 | 13 | 7 | 10 | 9 | 8 | 5 | 6 | 2 | 3 | 1
$E_{\phi}^{mn}\uparrow$ | .810 | .817 | .766 | .810 | .795 | .857 | .808 | .823 | .864 | .861 | .855 | .890 | .861 | .853 | .877 | .845 | .852 | .880 | .884 | .887 | .888 | .896
@$\mathcal{R}$ | 19 | 17 | 22 | 18 | 21 | 11 | 20 | 16 | 8 | 10 | 12 | 2 | 9 | 13 | 7 | 15 | 14 | 6 | 5 | 4 | 3 | 1
$E_{\phi}^{ad}\uparrow$ | .753 | .789 | .768 | .791 | .818 | .867 | .787 | .837 | .867 | .839 | .869 | .886 | .848 | .850 | .868 | .851 | .865 | .882 | .882 | .863 | .868 | .877
@$\mathcal{R}$ | 22 | 19 | 21 | 18 | 17 | 8 | 20 | 16 | 9 | 15 | 5 | 1 | 14 | 13 | 7 | 12 | 10 | 3 | 2 | 11 | 6 | 4
$F_{\beta}^{w}\uparrow$ | .517 | .575 | .509 | .569 | .544 | .633 | .557 | .601 | .631 | .629 | .677 | .686 | .635 | .667 | .660 | .655 | .666 | .673 | .684 | .680 | .672 | .693
@$\mathcal{R}$ | 21 | 17 | 22 | 18 | 20 | 13 | 19 | 16 | 14 | 15 | 5 | 2 | 12 | 8 | 10 | 11 | 9 | 6 | 3 | 4 | 7 | 1
$F_{\beta}^{mx}\uparrow$ | .633 | .699 | .634 | .679 | .609 | .691 | .658 | .672 | .691 | .704 | .729 | .743 | .712 | .742 | .725 | .733 | .738 | .732 | .738 | .752 | .743 | .759
@$\mathcal{R}$ | 21 | 14 | 20 | 17 | 22 | 16 | 19 | 18 | 15 | 13 | 10 | 3 | 12 | 5 | 11 | 8 | 7 | 9 | 6 | 2 | 4 | 1
$F_{\beta}^{mn}\uparrow$ | .582 | .651 | .582 | .635 | .593 | .681 | .615 | .654 | .679 | .671 | .715 | .723 | .679 | .712 | .701 | .702 | .711 | .715 | .721 | .718 | .710 | .728
@$\mathcal{R}$ | 22 | 17 | 21 | 18 | 20 | 12 | 19 | 16 | 13 | 15 | 6 | 2 | 14 | 7 | 11 | 10 | 8 | 5 | 3 | 4 | 9 | 1
$F_{\beta}^{ad}\uparrow$ | .526 | .593 | .555 | .589 | .588 | .673 | .573 | .639 | .667 | .640 | .707 | .703 | .652 | .671 | .676 | .667 | .681 | .699 | .705 | .682 | .680 | .698
@$\mathcal{R}$ | 22 | 17 | 21 | 18 | 19 | 10 | 20 | 16 | 12 | 15 | 1 | 3 | 14 | 11 | 9 | 13 | 7 | 4 | 2 | 6 | 8 | 5
$\mathcal{M}\downarrow$ | .061 | .047 | .058 | .047 | .051 | .042 | .051 | .043 | .043 | .045 | .038 | .036 | .042 | .035 | .040 | .037 | .035 | .037 | .035 | .037 | .036 | .033
@$\mathcal{R}$ | 22 | 18 | 21 | 17 | 20 | 13 | 19 | 15 | 14 | 16 | 10 | 5 | 12 | 4 | 11 | 9 | 3 | 8 | 2 | 7 | 6 | 1
|  Mean@$\mathcal{R}$ | 22 | 16 | 21 | 18 | 20 | 14 | 19 | 17 | 13 | 15 | 9 | 2 | 12 | 8 | 10 | 11 | 7 | 6 | 3 | 4 | 5 | 1

Competitors. We compare our model with 20 SOTA competitors (see Table 3),
including 8 SOD-based and 12 COD-based. For a fair comparison, all results
were either taken from the public website or produced by retraining the models
on the same training dataset with default settings.

\begin{overpic}[width=403.26341pt]{./Imgs/Quan_Curves_v3}
\put(20.0,35.5){\tiny{\text@underline{NC4K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{lyu2021simultaneously}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(50.5,35.5){\tiny{\text@underline{CAMO-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{le2019anabranch}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(81.0,35.5){\tiny{\text@underline{COD10K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2021concealed}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(20.0,67.1){\tiny{\text@underline{NC4K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{lyu2021simultaneously}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(50.5,67.1){\tiny{\text@underline{CAMO-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{le2019anabranch}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(81.0,67.1){\tiny{\text@underline{COD10K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2021concealed}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(20.0,98.8){\tiny{\text@underline{NC4K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{lyu2021simultaneously}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(50.5,98.8){\tiny{\text@underline{CAMO-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{le2019anabranch}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\put(81.0,98.8){\tiny{\text@underline{COD10K-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2021concealed}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\end{overpic} Figure 5: The PR curves (1st row), F-measure curves (2nd row),
and E-measure curves (3rd row) of COD-related competitors on three popular
datasets. The closer the PR curve is to the upper-right corner, the better the
performance is. The higher the F-measure/E-measure curve is, the better the
performance the better the model works. Best viewed in color.
\begin{overpic}[width=420.61192pt]{./Imgs/QualitativeResult_new_elite_v8-min}
\put(52.0,1.2){\footnotesize\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2021concealed}{\@@citephrase{(}}{\@@citephrase{)}}}}
\put(61.2,1.2){\footnotesize\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{lyu2021simultaneously}{\@@citephrase{(}}{\@@citephrase{)}}}}
\put(73.4,1.2){\footnotesize\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{li2021uncertainty}{\@@citephrase{(}}{\@@citephrase{)}}}}
\put(84.8,1.2){\footnotesize\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{zhai2021mutual}{\@@citephrase{(}}{\@@citephrase{)}}}}
\put(95.2,1.2){\footnotesize\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2020camouflaged}{\@@citephrase{(}}{\@@citephrase{)}}}}
\end{overpic} Figure 6: Visualization of popular COD baselines and the
proposed DGNet. Red boxes denote false-positive/-negative predictions. More
results are presented at https://github.com/GewelsJI/DGNet.
\begin{overpic}[width=429.28616pt]{./Imgs/BubbleBarFigv5.4-min}
\put(36.0,45.0){\tiny\text@underline{{CAMO-{Te}~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{le2019anabranch}{\@@citephrase{(}}{\@@citephrase{)}}}}}}
\end{overpic} Figure 7: (Left) We present the scatter relationship between the
performance ($F_{\beta}^{w}$) and parameters of all competitors on CAMO-Te
le2019anabranch . The larger the colored scatter point size, the heavier the
model parameters. (Right) We also report the parallel histogram comparison of
model’s parameters, MACs, and performance ($F_{\beta}^{w}$). Best viewed in
color.

###  4.2 Results and Analysis

Quantitative Results. As shown in Table 3, DGNet achieves the promising
performance in terms of all metrics. Especially, the gradient-based learning
strategy helps to improve the completeness of predictions, providing a 2.6%
gain of $F_{\beta}^{w}$ on CAMO-Te than rank@$1$ model SINetV2
fan2021concealed .

Quantitative Curves. As shown in Fig. 5, we plot the precision-recall (1st
row), F-measure (2nd row), and E-measure (3rd row) curves of all COD-related
competitors via varying with different thresholds. All comparisons show that
our curves with magenta solid/dotted lines are significantly better than other
methods on three datasets.

Qualitative Results. The visual comparison of four top-tier COD baselines and
our DGNet are shown in Fig. 6. Interestingly, these competitors fail to
provide complete segmentation results for the camouflaged objects touching the
image boundary. By contrast, our approach can precisely locate the target
region and provide exact predictions due to the gradient learning strategy.

Efficiency Analysis. To better unveil the trade-off, two instances
consistently obtain the best trade-off compared to existing competitors (see
Fig. 7). DGNet outperforms cutting-edge model SINetV2 fan2021concealed  with a
large margin ($F_{\beta}^{w}$: $+2.6\%$). Notably, our efficient instance
DGNet-S performs better than JCSOD li2021uncertainty , with 113.33M fewer
parameters. Besides, we also report the runtime comparison of all COD-related
competitors in Table 4, which are tested on an NVIDIA RTX TITAN GPU. It
clearly illustrates that DGNet-S and DGNet can achieve super real-time
inference speed (_i.e._ , 80 fps & 58 fps).

Table 4: Inference speed (fps) among 12 COD-related models and the proposed two instances (_i.e._ , DGNet-S and DGNet). Model | DGNet-S | DGNet |  SINetV2 fan2021concealed  |  JCSOD li2021uncertainty  |  LSR lyu2021simultaneously  |  R-MGL zhai2021mutual  |  S-MGL zhai2021mutual
---|---|---|---|---|---|---|---
Input Size | 352$\times$352 | 352$\times$352 |  352$\times$352 |  352$\times$352 |  352$\times$352 |  473$\times$473 |  473$\times$473
Speed (fps) | 80 | 58 | 68 | 43 | 31 | 9 | 13
Model |  PFNet mei2021camouflaged  |  UGTR yang2021uncertainty  |  TINet zhu2021inferring  |  C2FNet sun2021Context  |  BAS qin2021boundary  |  PraNet fan2020pranet  |  SINet fan2020camouflaged
Input Size |  416$\times$416 |  473$\times$473 |  352$\times$352 |  352$\times$352 |  288$\times$288 |  352$\times$352 |  352$\times$352
Speed (fps) | 78 | 15 | 50 | 68 | 31 | 73 | 63
Table 5: Ablation studies. #Para and #MACs denote the parameters and multiply-accumulate operations of the model. |  | Efficiency |  NC4K-Te lyu2021simultaneously  |  CAMO-Te le2019anabranch  |  COD10K-Te fan2021concealed
---|---|---|---|---|---
No. | Variant | #Para | #MACs | $\mathcal{S}_{\alpha}\uparrow$ | $F_{\beta}^{w}\uparrow$ | $\mathcal{M}\downarrow$ | $\mathcal{S}_{\alpha}\uparrow$ | $F_{\beta}^{w}\uparrow$ | $\mathcal{M}\downarrow$ | $\mathcal{S}_{\alpha}\uparrow$ | $F_{\beta}^{w}\uparrow$ | $\mathcal{M}\downarrow$
$\\#\textbf{S}$ | DGNet-S | 8.30M | 1.20G | .845 | .764 | .047 | .826 | .754 | .063 | .810 | .672 | .036
(a) Base Network $\rightarrowtail$ see Sec. 3.1 |  |  |
$\\#01$ | Base | 8.24M | 0.58G | .834 | .676 | .061 | .814 | .670 | .072 | .793 | .550 | .049
(b) Configuration of Dimensional Reduction $\rightarrowtail$ see Sec. 3.1 |  |  |
$\\#02$ | $C_{i}={16}$ | 8.00M | 0.81G | .842 | .758 | .048 | .824 | .749 | .066 | .806 | .663 | .037
$\\#03$ | $C_{i}={64}$ | 9.36M | 2.69G | .845 | .764 | .047 | .827 | .748 | .065 | .812 | .673 | .036
$\\#04$ | $C_{i}={128}$ | 13.30M | 8.55G | .847 | .768 | .046 | .828 | .751 | .062 | .810 | .672 | .036
(c) Network Decoupling Strategy $\rightarrowtail$ see Sec. 3.2 |  |  |
$\\#05$ |  w/ $\mathbf{X}_{2}$ | 8.24M | 0.59G | .840 | .712 | .055 | .822 | .701 | .074 | .805 | .597 | .043
(d) Should we use $\mathbf{Z}^{G}$ as supervision? $\rightarrowtail$ see Equ.(1) |  |  |
$\\#06$ |  w/ $\mathbf{Z}^{B}$ | 8.30M | 1.20G | .841 | .753 | .049 | .821 | .737 | .067 | .804 | .654 | .038
(e) Group Number $M$ $\rightarrowtail$ see Equ.(2) |  |  |
$\\#07$ | $M=1$ | 8.30M | 1.20G | .841 | .756 | .049 | .822 | .751 | .064 | .806 | .662 | .037
$\\#08$ | $M=4$ | 8.30M | 1.20G | .842 | .759 | .048 | .822 | .742 | .067 | .809 | .669 | .036
$\\#09$ | $M=16$ | 8.30M | 1.20G | .842 | .752 | .049 | .829 | .744 | .065 | .803 | .651 | .039
$\\#10$ | $M=32$ | 8.30M | 1.20G | .845 | .913 | .047 | .827 | .745 | .063 | .809 | .666 | .036
(f) Scaling Factors $N\in\\{N_{1},N_{2},N_{3}\\}$ $\rightarrowtail$ see Equ.(5) |  |  |
$\\#11$ | $\\{2,4,8\\}$ | 8.31M | 1.20G | .842 | .755 | .048 | .821 | .741 | .065 | .808 | .663 | .036
$\\#12$ | $\\{4,8,16\\}$ | 8.30M | 1.20G | .844 | .762 | .047 | .823 | .744 | .065 | .806 | .666 | .037
(g) More sub-branches in Soft Grouping Strategy $\rightarrowtail$ see Equ.(5) |  |  |
$\\#13$ | $N\in\\{4,8,16,32\\}$ | 8.30M | 1.20G | .844 | .760 | .048 | .829 | .748 | .064 | .811 | .669 | .037
$\\#14$ | $N\in\\{2,4,8,16,32\\}$ | 8.31M | 1.20G | .846 | .765 | .047 | .825 | .750 | .063 | .810 | .670 | .037
(h) Gradient-Induced Transition $\rightarrowtail$ see Equ.(6) |  |  |
$\\#15$ |  w/o $\mathcal{T}_{i}$ | 8.31M | 1.20G | .839 | .748 | .050 | .825 | .741 | .065 | .802 | .649 | .039

###  4.3 Ablation Study

We further ablate the core modules to verify the effectiveness of each part
and configuration. For ecological reasons, we select the DGNet-S as the base
model in this section.

Contribution of Base Network. In Table 5-(a), we remove the texture encoder
and GIT from DGNet-S and term it as the base network ($\\#01$). Compared to
it, our DGNet-S ($\\#\textbf{S}$) significantly improves the performance while
slightly increasing the model parameters by 0.06M.

Configuration of Dimensional Reduction. We change the channel $C_{i}$ to 16
($\\#02$), 32 ($\\#\textbf{S}$), 64 ($\\#03$), and 128 ($\\#04$) and find that
more parameters may lead to performance saturation. To achieve the best trade-
off between resource and speed, we choose $C_{i}$$=$$32$ as the default
setting.

Contribution of Network Decoupling Strategy. We explore the necessity of our
decoupling strategy. Inspired by ji2021fast , we replace the feature extracted
from the texture encoder with the low-level feature $\mathbf{X}_{2}$ from the
context encoder, which yields a single-stream network ($\\#05$). Notably, we
only change the extracting manner of texture features and preserve the
gradient-wise supervision for both variants (_i.e._ , $\\#05$ &
$\\#\textbf{S}$) to ensure unbiased ablation. Table 5-(c) demonstrates that
decoupling the network into two streams can improve the performance
($F_{\beta}^{w}$: +5.3% on CAMO-Te), which benefits from the modelling of
separated branches without feature ambiguity at different hierarchies.

\begin{overpic}[width=433.62pt]{./Imgs/AblationFeatureVis_v2-min}
\put(0.5,27.5){\footnotesize Camouflaged Image}
\put(33.0,27.5){\footnotesize(a) Object Boundary}
\put(68.0,27.5){\footnotesize(b) Object Gradient} \put(0.5,2.0){\footnotesize
Camouflaged Image} \put(37.0,2.0){\footnotesize(c) Before GIT}
\put(70.0,2.0){\footnotesize(d) After GIT} \end{overpic} Figure 8: Feature
visualizations of two core designs, including the supervision of object
gradient (1st row) and the GIT (2nd row).

Contribution of Object Gradient Supervision. We replace the gradient map
$\mathbf{Z}^{G}$ ($\\#\textbf{S}$) with the boundary mask $\mathbf{Z}^{B}$
($\\#06$) to supervise the context learning process. The improvement
($F_{\beta}^{w}$: $+1.7\%$ on CAMO-Te) of our gradient map supervision further
demonstrates the effectiveness. The first row of Fig. 8 presents the low-level
features extracted from the texture learning branch under different
supervision types. It shows that our solution can enforce the network to
capture the gradient-sensitive information inside the camouflaged object’s
body, where those pixels learn to draw the observer’s attention.

Table 6: Training DGNet-S under the supervision of texture label (TINet-Text zhu2021inferring ) and our object gradient label (DGNet-Grad). |  |  NC4K-Te
---
|  CAMO-Te
---
|  COD10K-Te
---
| $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$ | $\mathcal{M}$ | $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$ | $\mathcal{M}$ | $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$ | $\mathcal{M}$
w/ TINet-Text | .839 | .747 | .050 | .820 | .731 | .068 | .803 | .652 | .040
w/ DGNet-Grad | .845 | .764 | .047 | .826 | .754 | .063 | .810 | .672 | .036

We further experiment using the supervision of texture labels zhu2021inferring
(see Fig. 2), the results in Table 6 demonstrate that our gradient-supervision
manner (_i.e._ , w/ DGNet-Grad) is better than the texture-supervision (_i.e._
, w/ TINet-Text). Besides, our method is simpler and more efficient than
TINet, _e.g._ , DGNet-S (8.0M) vs. TINet (28.6M), DGNet-S (80 fps) vs. TINet
(50 fps). With such a compact design, we also achieve the new SOTA performance
on CAMO-Te, _e.g._ , DGNet-S ($\mathcal{S}_{\alpha}=0.826$), DGNet
($\mathcal{S}_{\alpha}=0.839$) vs. TINet ($\mathcal{S}_{\alpha}=0.781$).

Configuration of Group Numbers. In Table 5-(e), we report different variants
with respect to four group numbers $M$, which are equal to 1 ($\\#07$), 4
($\\#08$), 8 ($\\#\textbf{S}$), 16 ($\\#09$), and 32 ($\\#10$), respectively.
Note that $\\#07$ ($M=1$) means ungrouped candidate features, which causes
degraded performance ($F_{\beta}^{w}$: $-1.0\%$ on COD10K-Te). We empirically
choose $M=8$ with the best performance.

Configuration of Scaling Factors. We also discuss how scaling factors
$N\in\\{N_{1},N_{2},N_{3}\\}$ affect the model performance in Table 5-(f).
Compared with different configurations ($\\#11$: $\\{2,4,8\\}$ and $\\#12$:
$\\{4,8,16\\}$), our finer-grained factors ($\\#\textbf{S}$: $\\{8,16,32\\}$)
lead to better prediction performance. As shown in Fig. 4, we present the
feature visualization of three parallel features (_i.e._ ,
$\mathbf{A}^{N_{1}}_{i}$, $\mathbf{A}^{N_{2}}_{i}$, and
$\mathbf{A}^{N_{3}}_{i}$), where the network puts different attention weights
on different parts of the object insides. This also validates that parallel
residual learning can enhance the context feature from different group-aware
perspectives.

Do we need more sub-branches for soft grouping? As shown in Table 5-(g), we
set three ablative experiments for different sub-branches: three
($\\#\textbf{S}$: $N\in\\{8,16,32\\}$), four ($\\#13$: $N\in\\{4,8,16,32\\}$),
and five ($\\#14$: $N\in\\{2,4,8,16,32\\}$) sub-branches. The comparison
results unveil that more sub-branches would present unstable performance on
all the datasets.

Contribution of Gradient-Induced Transition. We replace the whole GIT in our
model with the naive channel-wise concatenation ($\\#15$: w/o
$\mathcal{T}_{i}$ in Table 5-(h)) to verify its effectiveness, which shows
that our DGNet-S equipped with GIT ($\\#\textbf{S}$: w/ $\mathcal{T}_{i}$) can
improve 2.3% $F^{w}_{\beta}$ on the COD10K-Te dataset. Moreover, as shown in
the second-row of Fig. 8, the model obtains a cleaner and finer representation
$\mathbf{Z}^{T}_{i}$ (_i.e._ , (d) after GIT) while suppressing the noises in
the background of $\mathbf{X}^{R}_{i}$ (_i.e._ , (c) before GIT). A clear
benefit of the adaptive aggregation of the context and texture cues in the
GIT.

###  4.4 Limitations

Table 7: Our method with different backbones, including EfficientNet tan2019efficientnet  (_i.e._ , EffNet-B1 & EffNet-B4) vs. MobileNet Howard_2019_ICCV  (_i.e._ , MobNet-S & MobNet-L). |  |  |  |  NC4K-Te
---
|  CAMO-Te
---
|  COD10K-Te
---
| #Para | MACs | $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$ | $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$ | $\mathcal{S}_{\alpha}$ | $F_{\beta}^{w}$
MobNet-S | 2.96M | 1.27G | .779 | .638 | .735 | .587 | .729 | .517
EffNet-B1 | 8.30M | 1.20G | .845 | .764 | .826 | .754 | .810 | .672
MobNet-L | 6.96M | 3.17G | .820 | .723 | .791 | .686 | .780 | .620
EffNet-B4 | 21.02M | 2.77G | .857 | .784 | .839 | .769 | .822 | .693

Efficient Backbone vs. Lightweight one. We further validate the potential
value of our method on limited hardware conditions by replacing the efficient
backbone, EfficientNet tan2019efficientnet , with a lightweight one, MobileNet
Howard_2019_ICCV . The results, as in Table 7, show that our method achieves
unsatisfactory performance with a lightweight backbone, _i.e._ , MobNet-S
(2.96M) and MobNet-L (6.96M), leaving a huge room for our future exploration.

Challenging Cases. Despite our method’s satisfactory performance, it may fail
in challenging camouflaged scenarios as follows. First, we argue that in the
proposed strategy it is still difficult to provide enough texture cues in the
limited small target region, resulting in false-positive predictions. As shown
in Fig. 9, such cases also easily confuse the rank@$1$ approach SINetV2
fan2021concealed , thus deserving further studies.

Second, we observe that not all the camouflaged objects with noticeable
gradient changes inside themselves. As shown in the first row of Fig. 10, our
method could segment a white rabbit with non-distinct gradient changes.
However, our method fails under extreme conditions, as in the second row of
Fig. 10, which has rare gradient cues. It needs to design by incorporating
more heuristic and learnable patterns for future improvements.

Additionally, we noticed a recently released COD method, ZoomNet pang2022zoom
, after the submission. As shown in Table 8, our DGNet surpass the ZoomNet
with a margin (_i.e._ , NC4K-Te: +1.3% $E_{\phi}^{mx}$ and CAMO-Te: +2.3%
$E_{\phi}^{mx}$), but fails to outperform ZoomNet on COD10K-Te. ZoomNet
occupies more computational costs (32.38M parameters) than our DGNet (21.02M
parameters). It inspires us to incorporate the zooming strategy into our
network for our future extension.

\begin{overpic}[width=433.62pt]{./Imgs/FailureCases-V1-min}
\put(0.0,-4.0){\footnotesize Camouflaged Image} \put(40.0,-4.0){\footnotesize
SINetV2~{}\cite[cite]{\@@bibref{Authors
Phrase1YearPhrase2}{fan2021concealed}{\@@citephrase{(}}{\@@citephrase{)}}}}
\put(69.0,-4.0){\footnotesize{DGNet~{}(Ours)}} \end{overpic} Figure 9: Hard
sample with small camouflaged object.

CamouflagedImageObjectGradientGround-TruthPrediction

Figure 10: Visual comparison of the object with non-distinct object gradient cues. Table 8: Performance comparison of our DGNet and the recent released ZoomNet pang2022zoom  on three testing datasets. |  |  |  |  NC4K-Te
---
|  CAMO-Te
---
|  COD10K-Te
---
| #Para | MACs | $\mathcal{S}_{\alpha}$ | $E_{\phi}^{mx}$ | $\mathcal{S}_{\alpha}$ | $E_{\phi}^{mx}$ | $\mathcal{S}_{\alpha}$ | $E_{\phi}^{mx}$
ZoomNet | 32.38M | 34.96G | .853 | .912 | .820 | .892 | .838 | .911
DGNet | 21.02M | 2.77G | .857 | .922 | .839 | .915 | .822 | .911

##  5 Downstream Applications

This section also assesses the generalization capabilities of three downstream
applications.

(a) Polyp Segmentation(b) Road Crack Detection(c) Transparent Object
Segmentation

Figure 11: Visualization results of three downstream applications. From top to
bottom: input image (1st row), ground-truth (2nd row), and prediction (3rd
row).

Polyp Segmentation. In the early diagnosis of colonoscopy, the low boundary
contrast between a polyp and its highly-similar surroundings significantly
decreases the detectability of colorectal cancer. To demonstrate the
generality of our method in the medical field, we follow the same benchmark
protocols as fan2020pranet  and retrain our DGNet on the training set of
Kvasir-SEG jha2020kvasir  and CVC-ClinicDB bernal2015wm  datasets. We use two
unseen test datasets: CVC-ColonDB bernal2012towards  and ETIS-LPDB
silva2014toward . Table 9 shows that our †DGNetconsistently surpasses four
cutting-edge polyp segmentation methods in four metrics, including
$\mathcal{S}_{\alpha}$, $E^{mx}_{\phi}$, $F^{w}_{\beta}$, and maximum Dice
score ($D^{mx}$). Notably, †DGNet denotes that we retrain DGNet on the task-
specific training dataset. Fig. 11 (a) shows the visualization results
generated by our †DGNet.

Table 9: Quantitative results on two popular polyp segmentation test datasets. |  CVC-ColonDB bernal2012towards  |  ETIS-LPDB silva2014toward
---|---|---
Baseline | $\mathcal{S}_{\alpha}\uparrow$ | $E^{mx}_{\phi}\uparrow$ | $F^{w}_{\beta}\uparrow$ | $D^{mx}\uparrow$ | $\mathcal{S}_{\alpha}\uparrow$ | $E^{mx}_{\phi}\uparrow$ | $F^{w}_{\beta}\uparrow$ | $D^{mx}\uparrow$
UNet ronneberger2015u  | .710 | .781 | .491 | .560 | .684 | .740 | .366 | .444
UNet++ zhou2019unet++  | .692 | .764 | .467 | .550 | .683 | .776 | .390 | .509
PraNet fan2020pranet  | .820 | .872 | .699 | .728 | .794 | .841 | .600 | .639
MSNet zhao2021automatic  | .838 | .883 | .736 | .766 | .845 | .890 | .677 | .736
†DGNet | .858 | .898 | .765 | .789 | .847 | .904 | .690 | .741

Defect Detection. Substandard products (_e.g._ , tiles, wood) will inevitably
incur unrecoverable economic losses in manufacturing. We further retrain our
†DGNet on the road crack detection dataset (_i.e._ , CrackForest
shi2016automatic ), using 60% of the samples for training and 40% for testing.
Fig. 11 (b) presents some visualization cases.

Transparent Object Segmentation. In daily life, intelligent agents such as
robots and drones need to identify unnoticeable transparent objects (_e.g._ ,
glasses, bottles, and mirrors) to avoid accidents. We also verify the
effectiveness of the retrained model †DGNet on the transparent object
segmentation task. For convenience, we re-organize the annotation of the
Trans10K xie2020segmenting  dataset from instance-level to object-level for
training. The visual results shown in Fig. 11 (c) further demonstrate the
learning ability of †DGNet.

##  6 Conclusion

We presented a novel deep gradient learning framework (DGNet) for efficiently
segmenting camouflaged objects. To extract the camouflaged features, we
proposed to decouple the task into two branches, a context encoder and a
texture encoder. We designed a novel plug-and-play module called gradient-
induced transition (GIT), acting as a soft grouping module to learn features
from these two branches jointly. This simple and flexible architecture showed
strong generalization capabilities on three challenging datasets compared to
the 20 SOTA competitors. In addition, our efficient version DGNet-S (8.3M & 80
fps) achieved an excellent performance-efficiency trade-off. Our solution also
produced visually appealing results for three further applications, including
polyp segmentation, defect detection, and transparent object segmentation,
which validates its practical application value.

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