# WEDGE: Web-Image Assisted Domain Generalization
for Semantic Segmentation

Namyup Kim1, Taeyoung Son2, Jaehyun Pahk1, Cuiling Lan3, Wenjun Zeng4, and
Suha Kwak1 1 Namyup Kim, Jaehyun Pahk and Suha Kwak are with POSTECH, Pohang,
Republic of Korea. {namyup, jhpahk<EMAIL_ADDRESS>Taeyoung Son is
with NALBI, Seoul, Republic of Korea<EMAIL_ADDRESS>Cuiling Lan is with
Microsoft Research Asia, Beijing, China<EMAIL_ADDRESS>Wenjun Zeng is
with EIT Institute for Advanced Study, Beijing, China<EMAIL_ADDRESS>
###### Abstract

Domain generalization for semantic segmentation is highly demanded in real
applications, where a trained model is expected to work well in previously
unseen domains. One challenge lies in the lack of data which could cover the
diverse distributions of the possible unseen domains for training. In this
paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme,
which is the first to exploit the diversity of web-crawled images for
generalizable semantic segmentation. To explore and exploit the real-world
data distributions, we collect web-crawled images which present large
diversity in terms of weather conditions, sites, lighting, camera styles, etc.
We also present a method which injects styles of the web-crawled images into
training images on-the-fly during training, which enables the network to
experience images of diverse styles with reliable labels for effective
training. Moreover, we use the web-crawled images with their predicted pseudo
labels for training to further enhance the capability of the network.
Extensive experiments demonstrate that our method clearly outperforms existing
domain generalization techniques.

##  I Introduction

Semantic segmentation has played crucial roles in many applications like
autonomous vehicle and augmented reality. Recent advances in this field are
mainly attributed to the development of deep neural networks, whose success
depends heavily on the availability of a large-scale annotated dataset for
training. However, building a large training dataset is prohibitively
expensive since it demands manual annotation of pixel-level class labels. To
mitigate this problem, synthetic image datasets have been introduced [1, 2].
They provide a large amount of labeled images for training at minimal cost of
construction. Also, they can simulate scenes that are rarely observed in the
real world yet must be considered in training (_e.g._ , accidents in
autonomous driving scenarios).

Figure 1: Overall framework of WEDGE. (1) Crawling real and task-relevant
images from the Web automatically. (2) Learning semantic segmentation while
transferring feature statistics of web images to features of synthetic
training images in the source domain. (3) Further training the model using
both source images and web-crawled images with predicted pseudo labels.

When learning semantic segmentation using synthetic images, it is essential to
close the gap between the synthetic and real domains caused by their
appearance differences so as to avoid performance degradation of learned
models on real-world images. Most of existing solutions to this issue belong
to the category of domain adaptation, which aims at adapting models trained on
synthetic images (_i.e._ , source domain) to real-world images (_i.e._ ,
target domain). In general, domain adaptation methods assume a single,
particular target domain and train models using images from both of labeled
source and unlabeled target domains [3, 4, 5, 6, 7, 8, 9, 10]. Unfortunately,
this setting limits applicability of learned models since, when deployed,
models can face multiple and diverse target domains (_e.g._ , geolocations and
weather conditions in the case of autonomous vehicle) that are latent at the
training stage.

As a more realistic solution to the problem, we study _domain generalization_
for semantic segmentation. The goal of this task is to learn models that
generalize well to various target domains without having access to their
images in training. A pioneer work [11] achieves the generalization by forcing
segmentation models to be invariant to random style variations of input image.
However, this method is costly since it applies an image-to-image translator
[12] to every synthetic image multiple times for the style randomization.
Moreover, random styles are given by a small number of images sampled from
ImageNet [13], and thus often irrelevant to target applications and hard to
cover a wide range of real-world image styles. Follow-up research is often
limited by the knowledge of ImageNet too. For example, Chen  _et al._ [14]
encourage the representations learned using synthetic images to be similar
with those of an ImageNet pretrained network, and Huang  _et al._ [15]
randomize synthetic images in a frequency space using a small subset of
ImageNet as references for stylization.

In this paper, we propose a WEb-image assisted Domain GEneralization scheme,
dubbed _WEDGE_ , which overcomes the limitations of the previous work by using
real and application-relevant images crawled from web repositories (_e.g._ ,
Flickr). The crawling process demands no or minimal human intervention as it
only asks search keywords that are determined directly by target application
(_e.g._ , “driving + road” for autonomous driving) or classes appearing in the
source domain images. Moreover, unlike those of ImageNet, the retrieved images
can be used for self-supervised learning as well as for stylization since they
are expected to be relevant to target application.

As illustrated in Fig. 1, WEDGE utilizes images crawled from the Web in two
different ways. First, it replaces neural styles of synthetic training images
with those of web-crawled images on-the-fly during training. This helps
enhance the generalization by giving illusions of diverse real images while
exploiting groundtruth labels of synthetic images. For this purpose, we
introduce a _style injection_ module that conducts the style manipulation in a
feature level at low cost. Since it is substantially more efficient than the
image-to-image translator used in [11], it allows to perform the stylization
on-the-fly using a large number of web images as style references in training.

Second, the web-crawled images are used as additional training data with
pseudo segmentation labels. To this end, the entire training procedure is
divided into two stages. In the first stage, a segmentation model is trained
with the style injection module, and the web-crawled images are used only for
stylization. The learned model is then applied to the web-crawled images to
estimate their pseudo labels. The second stage is identical to the former,
except that it also utilizes the web-crawled images as training data by taking
their pseudo labels as supervision.

To demonstrate the efficacy of WEDGE, we adopt each of the GTA5 [1] and
SYNTHIA [2] datasets as the source domain for training, and evaluate trained
models on three different real image datasets [16, 17, 18]. Experimental
results demonstrate that WEDGE enables segmentation models to generalize well
to multiple unseen real domains and clearly outperforms existing methods. In
summary, the contribution of this paper is three-fold:

  * •

To the best of our knowledge, WEDGE is the first that attempts to utilize web-
crawled images for domain generalizable semantic segmentation. These images
facilitate self training based on the realistic data which may better
approximate unseen testing domains.

  * •

We introduce style injection to domain generalizable semantic segmentation.
Through web-crawled images, it helps achieve the generalization by giving
diverse illusions of reality to the network being trained using labeled
synthetic images. Also, the superiority of our particular style injection
method over other potential candidates is demonstrated empirically.

  * •

WEDGE outperforms existing domain generalization techniques [11, 14, 19, 15,
20] in every experiment.

##  II Related Work

Domain generalizable semantic segmentation. The goal of domain generalization
is to learn models that well generalize to unseen domains [21, 22]. Early
approaches address this task mostly for classification [23, 24, 25, 26, 27,
28], but recent research demonstrates its potential for semantic segmentation
[11, 14, 26]. For example, Pan  _et al._ [26] tackle this problem by feature
normalization for learning domain invariant features, and Chen  _et al._ [14]
encourage the representation learned on a source domain to be similar with
that of an ImageNet pretrained model. Also, Yue  _et al._ [11] propose to
learn features invariant to random style variations of input, and establish an
evaluation protocol for the task. The main difference of ours from the
previous work is that ours explores and exploits real images on the Web which
enable models to experience a variety of real domains during training with no
human intervention.

Neural style transfer. A pioneer work by Gatys  _et al._ [29] shows that an
image style can be captured by the Gram matrix of a feature map, and Johnson
_et al._ [30] further enhance this idea to transfer a neural style to
arbitrary images. Huang  _et al._ [31] demonstrate that the channel-wise mean
and standard deviation of a feature map represent image style effectively.
Also, Nam and Kim [32] and Kim  _et al._ [33] propose to use different
normalization operations complementary to each other for style transfer.
Recently, content-aware style transfer methods [34, 35, 36] are emerged to
catch more details of local style patterns and to preserve content better. Huo
_et al._ [36] suppose that features passing through a network form a manifold
per each semantic region, and present a new style transfer technique based on
manifold alignment. Style injection in WEDGE is motivated particularly by the
techniques presented in [31, 36]. However, it is distinct from them in that it
aims to perform feature stylization, rather than image stylization.

Learning using data on the Web. Modern recognition models tend to be data-
hungry, yet the amount of training data is usually limited. Data on the Web
have been exploited to alleviate this issue. Early studies utilize web-crawled
images and videos for learning concept recognition by using their search
keywords as pseudo labels [37, 38, 39], and for object localization via
clustering images [37, 38] or by motion segmentation [40]. Motivated by recent
advances in pseudo labeling, a large-scale web data have been used for
supervised learning with their pseudo labels, which is known as webly
supervised learning. For image classification, Niu  _et al._ [41] present a
reliable way of utilizing search keywords as pseudo class labels. For semantic
segmentation, Hong  _et al._ [42] and Lee  _et al._ [43] compute pseudo labels
by segmenting web videos using attentions drawn by an image classifier.
Motivated by these, we present the first that makes use of web images for
domain generalization.

Figure 2:  Qualitative examples of web images and their pseudo labels
generated by the segmentation model with ResNet101 backbone trained by the
first stage of WEDGE. (a) Input images. (b) Pseudo segmentation labels. These
images demonstrate large diversity of the web images, which is vital for
achieving generalization to multiple latent test domains.

##  III Proposed Method

As shown in Fig. 1, WEDGE is divided into three steps:

  * 1.

Crawling images from web repositories automatically.

  * 2.

The first stage of training with style injection (SI). Learning a segmentation
model on the synthetic dataset while injecting styles of the web-crawled
images to its intermediate features for training.

  * 3.

The second stage of training using pseudo labels (PL). Further training the
model using the web-crawled images and their pseudo segmentation labels as
well as the synthetic dataset.

Details of each step are given in the remainder of this section.

###  III-A Crawling Images from the Web

We collect 4,904 images by crawling on Flickr, through the search keyword
“driving + road” to find images relevant to the target application scenario,
_i.e._ , autonomous driving. Examples of the collected images are presented in
Fig. 2.

Using these images for domain generalization has several advantages. First,
they offer a large variety of real image styles as illustrated in Fig. 2,
which _potentially cover testing domains_. This is vital for achieving
generalization to unseen domains. Second, they are not random but mostly
relevant to target applications due to the use of search keywords and thus can
be used for supervised learning given their pseudo labels. Last, they are
accessible with minimal human intervention since the crawling process above is
fully automated given a query. Note that previous work [11, 14, 15] also
exploits external images, those of ImageNet, for style randomization; the web
images are readily available like ImageNet and collected automatically, but
more relevant to target application scenarios thanks to the search keyword.

The web-crawled images are often different from synthetic domain images in
terms of semantic layout, and could partly contain irrelevant contents due to
the ambiguity of search keywords and errors of the search engine. WEDGE is
robust against these issues for the following reasons. In the first stage of
training, the style injection module exploits only styles of the web images
while disregarding their contents. In the second stage, irrelevant parts of an
image tend to be ignored in pseudo segmentation labels due to their unreliable
class predictions (_i.e._ , low confidence).

Figure 3:  Our style injection process. We first calculate the cross
correlation matrix $\Sigma$ between features of source and web images by the
cosine similarity $C(\cdot,\cdot)$. The optimal feature projection matrix for
style injection is computed by $M=UV^{\top}$, where $U$ and $V$ are obtained
by applying SVD to $F^{s}\mathbf{\Sigma}{F^{w}}^{\top}$. The source feature
map is then stylized by applying $M$, and the result $F^{s}M^{\top}$ is fed
into the next convoulution block.

###  III-B Stage 1: Learning with Style Injection (SI)

We propose a content-aware style injection method which transfers styles
between semantically similar features of synthetic and web-crawled images.
This method can inject diverse styles while better preserving the content of
an image than conventional methods relying on global feature statistics
(_e.g._ , AdaIN [31] or Gram matrix approximation [29]).

At each iteration of training the segmentation network, a synthetic image
$I^{s}$ is coupled with a randomly sample web image $I^{w}$. Let
$F^{d,l}\in\mathbb{R}^{H_{d}\times W_{d}\times C}$ be the feature map of
$I^{d}$ from the $l^{\textrm{th}}$ convolution block of the network where
$d\in\\{s,w\\}$. First, we compute a cross correlation between the $F^{s,l}$
and $F^{w,l}$ as an affinity matrix
$\mathbf{\Sigma}^{l}\in\mathbb{R}^{N_{s}\times N_{w}}$, where
$N_{d}=H_{d}W_{d}$ ($d\in\\{s,w\\}$):

$\displaystyle\mathbf{\Sigma}^{l}_{i,j}=\frac{F^{s,l}_{i}{F^{w,l}_{j}}^{\top}}{\|F^{s,l}_{i}\|\|F^{w,l}_{j}\|}.$
(1)

Then we find the projection matrix $M$ that minimizes the distance between
features of the projected feature map $F^{s,l}M^{\top}$ and web-crawled image
feature map $F^{w,l}$ weighted by the affinity matrix $\mathbf{\Sigma}^{l}$;
the role of the projection matrix $M$ is to project a synthetic feature onto a
subspace of the semantically similar web-crawled features. The objective
function is formulated by

$\displaystyle\min_{M}J(M)=\frac{1}{N_{\mathbf{\Sigma}}}\sum^{N_{s}}_{i=1}\sum^{N_{w}}_{j=1}\mathbf{\Sigma}_{ij}^{l}\|F^{s,l}M^{\top}-F_{j}^{w,l}\|,$
(2)

where $N_{\mathbf{\Sigma}}$ is sum of all elements of $\mathbf{\Sigma}^{l}$.
The closed form solution of Eq. (2) is given by Huo  _et al._ [36] as

$\displaystyle M=UV^{\top},$ (3)

where $U$ and $V$ are derived from singular value decomposition of
$F^{s,l}\mathbf{\Sigma}^{l}{F^{w,l}}^{\top}$, _i.e._ ,
$F^{s,l}\mathbf{\Sigma}^{l}{F^{w,l}}^{\top}=U\Lambda{V}^{\top}$. The synthetic
feature map is projected by the estimated $M$, and the result
$F^{s,l}M^{\top}$ is fed into the ${l+1}^{\textrm{th}}$ convolutional block;
Fig. 3 illustrates this process.

The overall pipeline of our style injection method follows that of the
manifold alignment based style transfer (MAST) [36]. However, unlike MAST that
computes a discrete affinity matrix using $k$ nearest neighbor assignment, our
method uses cosine similarity to compute the continuous affinity matrix in Eq.
(1). It considers similarity of all features to produce a content-aware
projection matrix and does not require hyperparameter $k$ nor
$\mathcal{O}(n^{2})$ time complexity to assign the nearest neighbors. Our
style injection process is designed as non-parametric, which enables effective
and low-cost feature adjustment.

The style injection is applied to multiple convolution blocks of the network,
in particular lower blocks since features of deeper layers are known to be
less sensitive to style variations. More details for implementation can be
found in Sec. IV-A. Injecting styles of real web images to synthetic training
images enlarges the training dataset by a multiple of the number of the web
images, which is tremendous regarding the size of the training dataset, as
well as making them look diverse and realistic in feature spaces. In addition,
there are several advantages of the content-aware style injection for domain
generalization of semantic segmentation over the conventional approaches [29,
31]. First, it enables style injection between semantically similar regions of
web-crawled and synthetic images, which is more natural and effective for
semantic segmentation. Second, since different styles are injected to
different semantic regions on an image, it helps keep boundaries between the
semantically different regions in style-injected features. We empirically
verify the superiority of our method over other potential style injection
candidates in Sec. IV-C.

Finally, the network is trained by the pixel-wise cross-entropy loss with the
segmentation label of the synthetic image $I^{s}$. Let $P^{s}$ and $Y^{s}$
denote the segmentation prediction and the groundtruth label of $I^{s}$,
respectively. The loss is then formulated as

$\mathcal{L}_{\textrm{seg}}(P^{s},Y^{s})=-\frac{1}{N}\sum_{i=1}^{H}\sum_{j=1}^{W}\sum_{k=1}^{C}Y_{ijk}^{s}\log
P_{ijk}^{s},$ (4)

where $N=H\times W$. Although this loss is applied only to the synthetic
domain, its gradients with respect to parameters will act as if the network
takes real domain images as input thanks to the style injection. Note that, in
this stage, $I^{w}$ is used only as a style reference.

###  III-C Stage 2: Learning Using Pseudo Labels (PL)

Once the first stage is completed, the learned model can be used to generate
pseudo labels of the web images. The pseudo labels allow us to exploit the web
images for supervised learning of the segmentation network, which further
enhances the generalization capability of the model by learning it directly on
a variety of real-world images.

Let $P^{w}\in\mathbb{R}^{H\times W\times C}$ be the segmentation prediction of
the network given $I^{w}$ as input. The pseudo segmentation label of $I^{w}$,
denoted by $\widetilde{Y}^{w}\in\\{0,1\\}^{H\times W\times C}$, is obtained by
choosing pixels with highly reliable predictions and labeling them with the
classes of maximum scores:

$\widetilde{Y}^{w}_{ijc}=\begin{cases}1,&\textrm{ if
}c=\underset{k}{\operatorname{argmax}}\ P^{w}_{ijk}\ \textrm{ and }\
h(P^{w}_{ij})<\tau\\\ 0,&\textrm{ otherwise}\end{cases},$ (5)

where $P^{w}_{ij}\in\mathbb{R}^{C}$ denotes the class probability distribution
of the pixel $(i,j)$, $h(\cdot)$ indicates the entropy, and $\tau$ is a
hyperparameter. Note that we regard the prediction $P^{w}_{ij}$ unreliable
when its entropy is high, _i.e._ , $h(P^{w}_{ij})\geq\tau$; in this case, the
pixel is assigned no label and ignored during training. Fig. 2 presents
examples of the pseudo labels.

The second stage of training utilizes both of the synthetic and the web images
for supervised learning. It is basically the same with the first stage
including the style injection, except that the segmentation loss is now
applied to $P^{w}$ as well as $P^{s}$. The total loss for the second stage is
thus given by a linear combination of two segmentation losses:

$\mathcal{L}(P^{s},Y^{s},P^{w},\widetilde{Y}^{w})=\mathcal{L}_{\textrm{seg}}(P^{s},Y^{s})+\mathcal{L}_{\textrm{seg}}(P^{w},\widetilde{Y}^{w}),$
(6)

where $\mathcal{L}_{\textrm{seg}}$ is the cross-entropy loss as given in Eq.
(4).

##  IV Experiments

In this section, we first present experimental settings in detail, then
demonstrate the effectiveness of WEDGE through extensive results.
Effectiveness of style injection, pseudo labeling, and other design choices of
WEDGE are investigated by ablation studies.

###  IV-A Experimental Setting

Source datasets. As a synthetic source domain for training, we use either the
GTA5 [1] or the SYNTHIA [2] datasets. GTA5 consists of 24,966 images and
shares the same set of 19 semantic classes with the real test datasets. Note
that we remove 36 images of smallest file sizes from the dataset since they
are non-informative, _e.g._ , blacked-out images. Meanwhile, SYNTHIA contains
9,400 images, whose annotations cover only 16 classes of the real test
datasets. Thus, we take only these 16 classes into account when evaluating
models trained on SYNTHIA.

Test datasets. As unseen target domains for evaluation, we choose the
validation splits of Cityscapes [16], BDD100k Segmentation (BDDS) [17] and
Mapillary [18]. Cityscapes and BDDS have 500 and 1,000 validation images,
respectively, and they are labeled for the same 19 classes. 2,000 validation
images of Mapillary are annotated for 66 classes. By following the protocol of
[44], we merge these classes to obtain the same 19 classes of Cityscapes.

Web-crawled images. From Flickr, we search for images whose widths are larger
than or equal to 760 pixels, and with no copyright reserved (_i.e._ , CC0) for
their public use in future work, using the search keyword “driving + road”. As
a result, 4,904 web images in total are collected. Note that, given these
conditions, the crawling process was done automatically, and the retrieved
images are used as-is without modification.

Networks and their training details. Following previous work [11], we adopt
DeepLab-v2 [45] with various backbone networks, ResNet50 and ResNet101 [46],
as our segmentation networks. They are first pretrained on ImageNet [13], and
then trained with the source dataset and our web images using SGD with
momentum of 0.9 and weight decay of ${5}\mathrm{e}{-4}$. The initial learning
rate is ${2}\mathrm{e}{-4}$ for the first stage (SI) and ${1}\mathrm{e}{-4}$
for the second stage (PL). $\tau$ in Eq. (5) is set to ${5}\mathrm{e}{-2}$ for
all experiments.

Where to inject styles. Styles of web images are injected into the feature
maps output by the $1^{\textrm{th}}$ and $2^{\textrm{nd}}$ residual blocks for
ResNet101 and ResNet50. The impact of injection points on performance is
analyzed in the accompanying video.

TABLE I:  Quantitative results in mIoU of domain generalization from (G)TA5 to
(C)ityscapes, (B)DDS, and (M)apillary.

Methods |  Backbone | G $\rightarrow$ C | G $\rightarrow$ B | G $\rightarrow$ M
---|---|---|---|---
IBN-Net [26] | ResNet50 | 29.6 | - | -
ASG [14] | ResNet50 | 31.9 | - | -
DRPC [11] | ResNet50 | 37.4 | 32.1 | 34.1
RobustNet [19] | ResNet50 | 36.6 | 35.2 | 40.3
WEDGE (Ours) | ResNet50 | 38.4 | 37.0 | 44.8
DRPC [11] | ResNet101 | 42.5 | 38.7 | 38.1
FSDR [15] | ResNet101 | 44.8 | 39.7 | 40.9
PinMemory [20] | ResNet101 | 44.9 | 39.7 | 41.3
WEDGE (Ours) | ResNet101 | 45.2 | 41.1 | 48.1

TABLE II:  Quantitative results in mIoU of domain generalization from
(S)YNTHIA to (C)ityscapes, (B)DDS, and (M)apillary.

Methods |  Backbone | S $\rightarrow$ C | S $\rightarrow$ B | S $\rightarrow$ M
---|---|---|---|---
DRPC [11] | ResNet50 | 35.7 | 31.5 | 32.7
WEDGE (Ours) | ResNet50 | 36.1 | 32.5 | 37.2
DRPC [11] | ResNet101 | 37.6 | 34.3 | 34.1
FSDR [15] | ResNet101 | 40.8 | 37.4 | 39.6
WEDGE (Ours) | ResNet101 | 40.9 | 38.1 | 43.1

###  IV-B Comparisons with the State of the Art

WEDGE is compared with existing domain generalization techniques, IBN-Net
[26], AGS [14], DRPC [11], RobustNet [19], FSDR [15] and PinMemory [20], using
two source domains {(G)TA5, (S)YNTHIA}, three test domains {(C)ityscapes,
(B)DDS, (M)apillary}, and two different backbone networks {ResNet50,
ResNet101}. As summarized in Table I and II, WEDGE clearly outperforms all the
previous arts in all the 12 experiments.

###  IV-C In-depth Analysis on WEDGE

Detailed performance analysis. To investigate the contribution of each
training stage in WEDGE, we measure its performance at each stage for all
experiments we have conducted so far. The results in Table III show that the
first stage using style injection most contributes to the performance in most
experiments, which demonstrates the effectiveness of using web-crawled images
and our style injection module for domain generalization. This achievement is
remarkable, especially when considering that web images could be erroneous or
irrelevant to the test domains. Thanks to our style injection modules, WEDGE
exploits diverse and realistic styles of web images while disregarding their
contents that may be irrelevant. The second stage also leads to non-trivial
performance improvement, particularly in the generalization from SYNTHIA to
BDDS, which imply the semantics or layouts of pseudo labels on SYNTHIA is more
similar to those of BDDS than the other datasets.

TABLE III:  Performance of WEDGE for domain generalization from (G)TA5 and
(S)YNTHIA to (C)ityscapes, (B)DDS, and (M)apillary.

| ResNet50 | ResNet101
---|---|---
|  |  Src.
---
only
| SI
---
(Stage 1)
| PL
---
(Stage 2)
|  Src.
---
only
| SI
---
(Stage 1)
| PL
---
(Stage 2)
G $\rightarrow$ C | 28.29 | 36.25 | 38.36 | 34.28 | 43.55 | 45.18
G $\rightarrow$ B | 29.16 | 36.30 | 37.00 | 32.96 | 40.35 | 41.06
G $\rightarrow$ M | 40.46 | 42.75 | 44.82 | 41.31 | 47.30 | 48.06
G${}_{\textrm{avg}}$ | 32.64 | 38.43 | 40.06 | 36.18 | 43.73 | 44.77
S $\rightarrow$ C | 27.06 | 35.28 | 36.09 | 29.96 | 38.22 | 40.94
S $\rightarrow$ B | 23.96 | 28.62 | 32.51 | 24.28 | 30.74 | 38.07
S $\rightarrow$ M | 31.67 | 36.49 | 37.18 | 36.19 | 38.61 | 43.10
S${}_{\textrm{avg}}$ | 27.56 | 33.46 | 35.26 | 30.14 | 35.86 | 40.70

Figure 4:  Qualitative results of WEDGE and its baseline using ResNet101
backbone and trained on the GTA5 dataset.

Qualitative results. The results in Fig. 4 show that the first stage of WEDGE
recovers most of the ill-classified pixels, and even finds out objects that
are missing in the baseline results. Also, its second stage further improves
the segmentation quality by correcting dotted errors and capturing fine
details of object shapes.

Comparison of style injection methods. We compare our style injection method
with other potential candidates based on existing style transfer techniques
[31, 36] to demonstrate its advantages. Note that these techniques are also
used for injecting styles of web images on-the-fly within the same framework.
As summarized in Table IV, while using AdaIN [31] and MAST [36] also improves
performance, our method achieves the best in both SI and PL stages except for
the GTA to BDDS case in the SI stage. Moreover, our method is more efficient
than MAST since it does not need $k$ nearest neighbor search, whose time
complexity is $\mathcal{O}(n^{2})$, that is required for MAST.

TABLE IV: Domain generalization performance of WEDGE with each variant of
style injection methods and ours.

SI methods | SI (Stage 1) | PL (Stage 2)
---|---|---
G$\rightarrow$C | G$\rightarrow$B | G$\rightarrow$M | G$\rightarrow$C | G$\rightarrow$B | G$\rightarrow$M
None (source only) | 34.28 | 32.96 | 41.31 | - | - | -
WEDGE$+$AdaIN [31] | 40.54 | 40.78 | 46.33 | 40.58 | 39.74 | 46.92
WEDGE$+$MAST [36] | 41.69 | 40.05 | 46.01 | 43.17 | 40.34 | 46.28
WEDGE (Ours) | 43.55 | 40.35 | 47.30 | 45.18 | 41.06 | 48.06

Impact of the number of web images. We investigate the impact of the number of
web images by evaluating performance of a segmentation model trained by WEDGE
with different numbers of web images.

Figure 5:  Domain generalization performance of WEDGE versus the number of web
images. In all experiments ResNet101 is used as backbone.

In Fig. 5, these models are compared in terms of segmentation quality on the
three target datasets. As shown in the figure, the generalization capability
of the model can be substantially improved by using only 1,000 web images,
while using the whole web dataset further improves performance. To be
specific, when using 1,000 web images, the average mIoU over the 3 test
datasets is 42.4%, lacking only 2.4% compared to the average performance of
our final model. The results also indicate that WEDGE consistently enhances
the generalization performance when increasing the number of web-crawled
images.

Impact of using task-relevant web images. The contribution of our crawling
strategy is demonstrated by comparing WEDGE with its variants relying on other
types of style references instead of the task-relevant web images.
Specifically, we utilize images sampled from the ImageNet dataset and web
images crawled by the search keyword “indoor”, both of which are irrelevant to
the target task. Also, the number of style references is set to 5,000 for fair
comparisons to WEDGE. Note that since these images are totally irrelevant to
the target task, they are not suitable for pseudo labeling thus are used only
for style injection. As shown in Table V, our method using task-relevant web
images (_i.e._ , “driving+road”) clearly outperforms the others. Using the
real yet irrelevant images improves performance, suggesting the robustness of
our method, but the results are still inferior to those of our method, meaning
that our crawling strategy is useful and using relevant images matters.

TABLE V:  Domain generalization performance of WEDGE with different types of
style reference.

Style reference |  G $\rightarrow$ C | G $\rightarrow$ B |  G $\rightarrow$ M
---|---|---|---
None (source only) | 34.28 | 32.96 | 41.31
ImageNet | 42.42 | 38.24 | 47.37
Web images: “indoor” | 42.28 | 40.47 | 47.69
Web images: “driving+road” | 45.18 | 41.06 | 48.06

##  V Conclusion

We have introduced WEDGE, the first web-image assisted domain generalization
scheme for learning semantic segmentation. It explores and exploits web images
that depict large diversity of real world scenes, which potentially cover
latent test domains and thus help improve generalization capability of trained
models. It utilizes the web-crawled images in two effective ways, namely style
injection and pseudo labeling, which lead to consistent performance
improvement on various test domains. WEDGE clearly outperformed existing
domain generalization techniques in all experiments. Also, extensive ablation
studies demonstrated that WEDGE is able to utilize noisy and irrelevant web-
crawled images reliably and is not sensitive to their number in training.

Acknowledgement. This work was supported by Samsung Research Funding &
Incubation Center of Samsung Electronics under Project Number SRFC-IT1801-52.
This work was done while Namyup Kim was working as an intern at Microsoft
Research Asia.

Appendix

This material presents implementation details and experimental results omitted
from the main paper due to the space limit. First, Sec. VI demonstrates
advantages of the feature-level style injection compared to the image-level
style transfer. Sec. VII examines how much sensitive WEDGE is to the hyper-
parameter $\tau$ and Sec. VIII investigates the impact of style injection
points by an ablation study. Sec. IX discusses using domain-specific web
images and Sec. X provides more qualitative examples of the web-crawled images
we use. Then Sec. XI presents more qualitative results of WEDGE.

##  VI Advantages of Feature-level Style Injection

Since WEDGE injects style representations in feature levels, one may wonder
its advantages over image-level style transfer. This section demonstrates the
effectiveness of WEDGE, especially its style injection (SI) module, compared
to image-level style transfer. To this end, we adopt AdaIN [31], exploiting
feature statistics as style representation like WEDGE. We generate 100,000
stylized GTA5 [1] images by AdaIN using web-crawled images as style
references; a few examples are shown in Fig. 6. We then train a segmentation
model on the stylized GTA5 dataset.

As summarized in Table VI, we compare the model of the 1${}^{\textrm{st}}$
stage of WEDGE (SI only) with the model trained on the stylized GTA5 images
generated by AdaIN. The results show WEDGE using SI outperforms AdaIN on all
experimental settings except G$\rightarrow$C and G$\rightarrow$B with VGG16
[46]. Moreover, our feature-level approach has another benefit over the image-
level counterpart in terms of efficiency. AdaIN requires an additional network
for style transfer. On the other hand, SI in WEDGE is non-parametric and
adjusting feature statistics of source images by those of web-crawled images,
thus demands a much lower computational cost than AdaIN.

##  VII Sensitivity to Hyper-parameter $\tau$

This section demonstrates the impact of the thresholding parameter s$\tau$ on
the quality of pseudo labels in terms of semantic segmentation performance.
Specifically, pseudo segmentation labels are generated using $\tau$, which is
a hyper-parameter that filters out unreliable predictions. To this end, we
design multiple variants of our model that are trained from different pseudo
segmentation labels generated from various $\tau$. The pseudo segmentation
label of $I^{w}$, denoted by $\widetilde{Y}^{w}\in\\{0,1\\}^{H\times W\times
C}$, is obtained by choosing pixels with highly reliable predictions and
labeling them with the classes of maximum scores:

$\widetilde{Y}^{w}_{ijc}=\begin{cases}1,&\textrm{ if
}c=\underset{k}{\operatorname{argmax}}\ P^{w}_{ijk}\ \textrm{ and }\
h(P^{w}_{ij})<\tau\\\ 0,&\textrm{ otherwise}\end{cases},$ (7)

where $h(\cdot)$ indicates the entropy, $P^{w}_{ij}\in\mathbb{R}^{C}$ denotes
the class probability distribution of the pixel $(i,j)$, and $\tau$ is a
hyper-parameter. We sample $\tau$ from {0.1, 0.05, 0.01, 0.005}, where
$\tau=0.05$ means our model in the main paper.

As summarized in Fig. 7, the results are marginally different across the
variation of the hyper-parameters except 0.1, but the setting we adopt in the
paper is slightly better than the others. Examples of the pseudo labels of
web-crawled images are presented in Fig. 8, which demonstrates both pros and
cons of different threshold values. With a moderate thresholding (_e.g._ ,
0.1), the pseudo labels cover more real texture or parts of an object but have
more noisy semantic labels. With a strict thresholding, on the other hand, the
pseudo labels have more accurate semantic information but cover smaller
regions of web-crawled images. The thresholding hyper-parameter we choose is
in the middle, and leads to the best performance.

Figure 6: Examples of GTA5 images stylized by AdaIN. (a) Content images. (b)
Style images. (c) Stylized images. TABLE VI: Quantitative results in mIoU and
parameters of domain generalization from (G)TA5 to (C)ityscapes , (B)DDS and
(M)appillary.

Methods | Backbone | Params | G $\rightarrow$ C | G $\rightarrow$ B | G $\rightarrow$ M
---|---|---|---|---|---
Deeplab-v2 [45] +AdaIN | VGG16 | 53.1M | 35.33 | 34.49 | 40.17
ResNet50 | 48.6M | 33.31 | 34.02 | 38.55
ResNet101 | 67.6M | 39.41 | 36.20 | 41.50
WEDGE (SI) | VGG16 | 29.6M | 35.33 | 34.48 | 40.54
ResNet50 | 25.1M | 36.25 | 36.30 | 42.75
ResNet101 | 44.0M | 43.55 | 40.35 | 47.30

Figure 7:  Performance of our ResNet101 backbone model trained with different
thresholded pseudo labels. $\textrm{G}_{avg}$ is the average performance of
the three test domains. The performances across the set of hyper-parameters
$\tau$ except 0.1 are marginally different.

##  VIII Details of Style Injection

Style representations of web-crawled images are injected into the feature maps
output by $1^{\textrm{th}}$ and $2^{\textrm{nd}}$ residual blocks for both
ResNet101 and ResNet50 [46], and those of $2^{\textrm{nd}}$ and
$3^{\textrm{rd}}$ blocks for VGG16 [47]. To verify the effectiveness of our
method, this section presents ablation studies with various combinations of
injection points. We present experimental results with ResNet101 combined with
six different combinations in Table VII. The results show that semantic
segmentation performance is degraded when $4^{\textrm{th}}$ residual block is
included. We suspect this is because deeper features are known to contain
semantic information rather than styles, which makes them inappropriate for
style injection. As a result, using the output feature maps from the
{$1^{\textrm{st}}$, $2^{\textrm{nd}}$} residual blocks turn out to be the most
effective combination for ResNet101. Therefore, we choose our injection points
based on these observations when applying style injection to other backbone
networks.

TABLE VII: Performance of the models with ResNet101 backbone on the setting
from (G)TA5 to (C)ityscapes, (B)DDS and (M)appillary.

Style injection points | G $\rightarrow$ C | G $\rightarrow$ B | G $\rightarrow$ M | Average
---|---|---|---|---
1 | 2 | 3 | 4
✓ | ✓ |  |  | 43.55 | 40.35 | 47.30 | 43.73
✓ | ✓ | ✓ |  | 44.03 | 39.30 | 47.30 | 43.54
✓ | ✓ | ✓ | ✓ | 41.01 | 38.49 | 46.46 | 41.99
| ✓ | ✓ |  | 42.00 | 39.03 | 44.93 | 41.99
|  | ✓ | ✓ | 37.92 | 35.02 | 38.47 | 37.20
| ✓ | ✓ | ✓ | 38.64 | 35.19 | 41.39 | 38.44

TABLE VIII:  Domain generalization performance of WEDGE with different types
of style reference. In all experiments ResNet101 is used as backbone.

Method | Keywords | G$\rightarrow$C | G$\rightarrow$B | G$\rightarrow$M | Average
---|---|---|---|---|---
WEDGE (SI) | driving + snow | 42.44 | 39.78 | 45.13 | 42.45
driving + rain | 42.87 | 38.13 | 45.95 | 42.32
driving + fog | 43.40 | 40.22 | 46.54 | 43.39
driving + road | 43.55 | 40.35 | 47.30 | 43.73

##  IX Comparison with using domain-specific web images

Since our task at hand is domain generalization that assumes arbitrary target
domains, we employ the keyword that does not indicate any specific domains.
Nevertheless, we experiment with the keywords “driving + {rain, show, fog}”.
As summarized in Table VIII, these specific keywords are not as useful as the
general one “driving + road” in our framework.

##  X Examples of Web-crawled Images

This section exhibits a part of our web-crawled dataset. Qualitative examples
of the web-crawled images are presented in Fig. 11, which demonstrates the
diversity of the images in terms of time, geolocation, weather condition, and
so on. Such diversity enables WEDGE to achieve the generalization to latent
real domains. Note that these images often depict entities and semantic
layouts that are irrelevant to those of source (and target) domains. However,
they are used as-is with no manual filtering process since the style injection
and pseudo labeling of WEDGE offer reliable and effective ways to utilize
them.

##  XI More Qualitative Results

We present qualitative examples of semantic segmentation results by WEDGE for
both of Stage 1 (SI) and Stage 2 (SI+PL) in Fig. 9 and Fig. 10. In these
figures, the semantic segmentation results are color-coded by following the
standard Cityscapes color map [16]; the colors associated to the classes are
exhibited in Table IX.

road | sidewalk | building | wall
---|---|---|---
fence | pole | traffic light | traffic sign
vegetation | terrain | sky | person
rider | car | truck | bus
train | motorbike | bicycle |

TABLE IX: The color code of classes on the test datasets.

Figure 8: Examples of the pseudo labels with the different thresholding hyper-
parameter $\tau$. (a) Input images. Pseudo labels with (b) $\tau=0.1$. (c)
$\tau=0.05$ (Ours). (d) $\tau=0.01$. (e) $\tau=0.005$. Figure 9:  Qualitative
results of WEDGE and its baseline using ResNet101 backbone and trained on the
GTA5 dataset.  Figure 10:  Qualitative results of WEDGE and its baseline using
ResNet101 backbone and trained on the GTA5 dataset.  Figure 11:  500 random
samples of the web-crawled images used in WEDGE.

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