Title: Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation
Venue: NeurIPS 2024
Accepted: yes
Keywords: Semantic Segmentation

Domain Generalization

Vision Foundation Model

Haar Wavelets
Abstract: The emerging vision foundation model (VFM) has inherited the ability to generalize to unseen images.
Nevertheless, the key challenge of domain-generalized semantic segmentation (DGSS) lies in the domain gap attributed to the cross-domain styles, i.e., the variance of urban landscape and environment dependencies.
Hence, maintaining the style-invariant property with varying domain styles becomes the key bottleneck in harnessing VFM for DGSS. 
The frequency space after Haar wavelet transformation provides a feasible way to decouple the style information from the domain-invariant content, since the content and style information are retained in the low- and high- frequency components of the space, respectively. 
To this end, we propose a novel Frequency-Adapted (FADA) learning scheme to advance the frontier.
Its overall idea is to separately tackle the content and style information by frequency tokens throughout the learning process.
Particularly, the proposed FADA consists of two branches, i.e., low- and high- frequency branches. The former one is able to stabilize the scene content, while the latter one learns the scene styles and eliminates its impact to DGSS. 
Experiments conducted on various DGSS settings show the state-of-the-art performance of our FADA and its versatility to a variety of VFMs.
Source code is available at \url{https://github.com/BiQiWHU/FADA}.

Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation

Abstract

The emerging vision foundation model (VFM) has inherited the ability to generalize to unseen images. Nevertheless, the key challenge of domain-generalized semantic segmentation (DGSS) lies in the domain gap attributed to the cross-domain styles, e.g., the variance of urban landscape and environment dependencies. Hence, maintaining the style-invariant property with varying domain styles becomes the key bottleneck in harnessing VFM for DGSS. The frequency space after Haar wavelet transform provides a feasible way to decouple the style information from the domain-invariant content, since the content and style information is retained in the low- and high-frequency components of the space, respectively. To this end, we propose a novel Frequency-Adapted (FADA) learning scheme to advance the frontier. Its overall idea is to separately tackle the content and style information by frequency tokens throughout the learning process. Particularly, the proposed FADA consists of two branches, i.e., low- and high-frequency branches. The former is able to stabilize the scene content, while the latter learns the scene styles and eliminates its impact to DGSS. Experiments conducted on various DGSS settings show the state-of-the-art performance of our FADA and its versatility to a variety of VFMs. Source code is available at https://github.com/BiQiWHU/FADA .

1 Introduction

Most existing semantic segmentation tasks assume that the training and inference images follow the independent and identical distribution (i.i.d.) [12, 13, 36, 35, 34, 81, 54, 44, 74], which is far from reality. Domain-generalized semantic segmentation (DGSS) aims to infer robust pixel-wise semantic predictions on arbitrary unseen target domains when a segmentation model is trained on the source domain (as illustrated in Fig. 1a). Compared with general domain generalization tasks, the feature distribution discrepancy between the source domain and unseen target domains in the context of DGSS holds some unique factors. Specifically, the cross-domain images in DGSS usually share the same content information ( i . e ., common semantic categories in driving scenes), while the cross-domain styles ( i . e ., urban landscape, weather, lighting conditions, etc .) mainly account for the feature distribution difference [65, 49, 18, 11, 72, 6, 23, 43, 24].

Existing DGSS methods can be summarized into three categories. The first category intends to decouple the style information from the scene representation [55, 30, 14, 56, 78, 71, 58], but does not

CityScapes

BDD

Mapillary

GTAV

SYNTHIA

generalization to unseen domains

cross-domain style variation

sign

pedestrian

cars

trees

… …

dawn

sunny

night

rainy

… …

Varied Styles

LL

LH

HL

HH

B

0.6

0.4

0.2

M G

S

B

0.6

0.4

0.2

M G

S

B

0.6

0.4

0.2

M G

S

B

0.6

0.4

0.2

M G

S

C

C

B

M

G

S

C

B

M

S

G

C

B

S

M

G

B

M

G

S

+

(a) Domain Generalized Semantic Segmentation

(b) Domain Distribution after Haar Transformation

ensure a strong content representation ability. The second category, on the contrary, directly focuses on the content representation ability regardless of the cross-domain style variations [19, 4, 7]. Such methods were recently developed much owing to the stronger pixel-wise representation ability of mask attention [12, 13]. The third category focuses on enriching the styles as much as possible during training [82, 41, 83]. However, the content representation ability is rarely taken into consideration.

The emerging vision foundation model (VFM) [62, 39, 20, 37], with strong generalization ability inherited from a large quantity pre-trained images, provides a new possible paradigm for DGSS. Under the realm of parameter-efficient fine-tuning, the hypothesis, i . e ., a foundation model relies on the low intrinsic dimension [42, 1] to adapt to the downstream tasks, exemplified by low-rank adaptation (LoRA) [28], has shown great success, where the key idea is to inject trainable rank decomposition matrices into each layer. More recently, the low-rank adaptation paradigm has demonstrated to be feasible to fine-tune the VFM for DGSS [69]. Unfortunately, the style-invariant properties of VFM, which is a fundamental problem for the extraction of domain generalized semantics, remain unexplored.

An ideal style invariant low-rank adaptation is supposed to discern the subtle low intrinsic dimension while does not pose shift on the fragile and frozen VFM features. The prior DGSS methods learned style invariance from the fully trained image features (e.g., instance normalization [55, 30] and instance whitening [14, 56, 71, 58]), which are more likely to collapse and less feasible. Therefore, we adapt the low-rank adaptation to the frequency space, where the style and content have been separated to high-frequency and low-frequency components [29, 46, 40, 77, 67], respectively.

In this paper, we present a novel F requencyAda pted learning scheme, dubbed FADA, to push this frontier. Its conceptual idea is to adapt the style and content representation from the VFM features separately in the frequency space. After transforming the frozen VFM features to the frequency space by the Haar wavelet transform [60], the low-frequency branch exploits the scene content from frozen VFM features by the learnable low-frequency tokens. In contrast, in the high-frequency branch, the high-frequency token features are implemented using the instance normalization operation, so that the representation becomes invariant to the scene style.

Notably, the proposed FADA introduces two new research lines. Firstly, the possibility of learning lowrank adaptation in the frequency space is explored, which can further benefit other VFM downstream tasks strongly related to the style and content representation. Secondly, it demonstrates the potential of harnessing the Haar wavelet transform for DGSS, which can also inspire advancements in general visual domain generalization.

Concretely, our contributions can be summarized as follows:

We propose a F requencyAda pted learning scheme, dubbed FADA, to fine-tune VFMs for domain-generalized semantic segmentation.

The proposed FADA, aided by the Haar wavelet guidance to mine the style-invariant property of VFM, is versatile to a variety of VFMs.

Experimentally, the proposed FADA significantly outperforms the state-of-the-art DGSS methods, and yields an improvement up to 2.9% mIoU over the contemporary REIN [69].

2 Related Work

Domain Generalization handles the challenging setting where the feature distribution of an arbitrary target domain is not identical to that of the source domain. It has been extensively studied in the past few years. Multiple techniques, to namely a few, optimal transport [22, 79], batch normalization [66], causal inference [48, 47, 25], discrepancy regularization [80, 68, 17, 5], and uncertainty modeling [59, 70], have been proposed. Furthermore, domain generalization via unsupervised learning [26, 27] or from a single source domain [61, 59, 84, 75] has also been recently studied.

Domain Generalization by Frequency Decoupling has drawn increasing attention. Its general idea rests in that the style and content have been demonstrated on high-frequency and low-frequency components [29, 46, 40, 77, 67, 7, 6], respectively. Most of these methods implement Fast Fourier Transform (FFT) to transfer the image to the frequency space, and then represent the style and content by the amplitude (high-frequency) and phase (low-frequency) components, respectively. However, to the best of our knowledge , 1) leveraging Haar wavelet for domain generalization; and 2) enhancing the generalization ability of VFM features via frequency space have been rarely explored. Compared with other frequency analysis methods such as FFT, the orthogonal property of Haar wavelet basis leads to a stronger decorrelation [50]. In the context of domain generalization, it indicates a better separation between low- and high-frequency components and deserves exploration.

Domain Generalized Semantic Segmentation (DGSS) in the CNN era either decouple the style information [55, 30, 56, 14, 58, 71, 78] or enrich the style diversity [41, 82, 83, 45, 52]. With the rapid development of Vision Transformer (ViT), recent DGSS methods usually leverage the masked attention mechanism [12, 13] to enhance the content representation [19, 8, 7]. Later, the masked attention is used to decode the frozen contrastive image-text pre-trained features [33], and REIN [69] fine-tunes the VFM under the low-rank adaptation paradigm. However, the style invariant properties of VFM, which are the key of the DGSS representation, remain unexplored.

3 Preliminary

3.1 Low-Rank Adapted VFM

To fine-tune a VFM with parameter efficiency, the low intrinsic dimension [42, 1] assumes that a VFM relies on the intrinsic low-dimension in the frozen VFM features to adapt to the downstream tasks. Inspired by this, the low-rank adaptation (LoRA) paradigm [28] is devised to inject trainable rank decomposition matrices into each layer. Given a VFM with N sequential layers (denoted as L 1 , L 2 , · · · , L N ), each layer corresponds to a pre-trained weight matrix of W 1 , W 2 , · · · , W N , i . e ., W i ∈ R c × c . The frozen features from layer L i are denoted as f i ∈ R c × n . Particularly, for the first layer L 1 , f 1 is generated by f 1 = W 1 x . Here x denotes the image embedding, n denotes the patch number, and c denotes the channel size.

Denoting the learnable weight matrix as ∆ W i ∈ R c × c and the input of layer L i as f i , the feature propagation from the layer L i to L i +1 can be formulated as f i +1 = W i f i + ∆ W i f i . Then, we assume that the learnable weight matrix ∆ W i can be formulated as a low-rank decomposition, i . e ., ∆ W i = BA , where B ∈ R c × r and B ∈ R r × c ( r ≪ c ). More recently, REIN [69] specifies this paradigm in DGSS by transferring the learnable matrix term ∆ W i into a learnable token T i followed by a MLP M i ( · ) , denoted as f i +1 = W i f i +M i ( T i ( W i f i )) , where T i ∈ R m × c and m is the token length. This modification allows a significance reduction of the token length (from a thousand magnitude c to a hundred or even ten magnitude m ), which can alleviate Curse of Dimensionality and allow each token to be better connected to the instances in an image [69]. Our low-rank adaptation is implemented on T i , i . e ., T i = A i B i , where A i ∈ R m × r and B i ∈ R r × c ( r ≪ min( m,c ) ).

ҧ

ҧ

ҧ

Source Domain

Vision Foundation Model

Target Domain

… …

Prediction

Prediction

… …

Segmentation

Decoder

Layer

𝐿1

Layer

𝐿2

… ...

Layer

𝐿3

Layer

𝐿𝑁-2

Layer

𝐿𝑁

Layer

𝐿𝑁-1

Inverse Haar Wavelet Transform

LL

LH, HL, HH

Haar Wavelet Transform

...

...

low-frequency token

𝑇 𝑖

𝐿

high-frequency token

𝑇

𝑖

𝐻

MLP

MLP

Instance

Norm

MLP

MLP

training

point-wise product

inference

adding

𝑆 𝑖

𝐻

𝑆 𝑖

𝐿

𝑓 𝑖

𝐿𝐻

𝑓 𝑖

𝐻𝐿

𝑓 𝑖

𝐻𝐻

𝑓 𝑖

𝐿𝐿

𝑓 𝑖

𝐿𝐿

ሚ

𝑓 𝑖

𝐿𝐿

𝑓 𝑖

𝐿𝐻

𝑓 𝑖

𝐻𝐿

𝑓 𝑖

𝐻𝐻

ሚ

𝑓 𝑖

𝐿𝐻

ሚ

𝑓 𝑖

𝐻𝐿

ሚ

𝑓 𝑖

𝐻𝐻

3.2 Haar Wavelet Transform

The Haar transform [60] cross-multiplies a function with various shifts and stretches, which has been demonstrated to be effective in applications, such as signal and image processing.

Definition 1. Haar Scaling Function. Given an input signal x , the Haar scaling function is mathematically defined as

Let V 0 denote the space of all functions of the form ∑ k ∈ Z a k ϕ ( x -k ) , where k ∈ Z is an arbitrary integer, and a k ∈ R . As each element of V 0 is zero outside a bounded set, such a function a k ϕ ( x -k ) has finite or compact support .

Definition 2. Basis of the Step Function Space. Given an arbitrary nonnegative integer j ∈ Z + 0 , Let V j denote the step function space at the level j , which is spanned by the set

Definition 3. Haar Wavelet Function. The Haar wavelet is the function ψ ( x ) = ϕ (2 x ) -ϕ (2 x -1) .

For more details of the properties of the Haar transform, please refer to the supplementary material.

4 Methodology

Fig. 2 gives an overview of the proposed FADA. After each frozen VFM layer, it consists of three key steps, namely, low-/high-frequency decomposition (in Sec. 4.1), low-frequency adaptation (in Sec. 4.2), and high-frequency adaptation (in Sec. 4.3). Finally, the frequency components are fused together and transferred from the frequency space back to the spatial space, and then fed to the next VFM layer.

4.1 Low-/High-Frequency Decomposition

Orthogonal property (in Sec. 3.2) leads to the result of a strong decorrelation [50]. This property after the Haar wavelet function allows a better separation between low- and high-frequency components of input signal than other frequency analysis methods such as Fourier transform. In the context of DGSS, the domain gap is caused by the cross-domain style variation, while the cross-domain content is stable [14, 78, 71, 58]. As it has been well documented that the style and content are predominate

ҧ

in the high-frequency and low-frequency components, respectively [29, 46, 40, 77, 67], a feasible way to explore the style-invariant properties is to mitigate the variation of high-frequency components in the VFM features. Therefore, the first step is to decompose the frozen VFM feature f i into the lowand high-frequency components, respectively.

In our work, we exploit the Haar wavelet transform to decouple the low- and high-frequency components, where four kernels, namely, LL T , LH T , HL T , HH T , are given by

As discussed in prior works [60, 3], the LL T kernel captures the average of the pixel responses, which is more robust to the scene content and therefore preserves more low-frequency components. In contrast, by taking the differences between adjacent pixels into account, the LH T , HL T and HH T kernels tend to preserve the details from the horizontal, vertical and diagonal directions, respectively. These details are more related to the structures, edges, etc , which attribute to the style.

For a layer L i , we implement the Haar wavelet transform on the frozen VFM feature W i f i by the above four kernels LL T , LH T , HL T and HH T , respectively. The f LL i component filtered by LL T captures more scene content, and in contrast the f LH i , f HL i and f i HH components filtered by LH T , HL T and HH T capture more style information. This decomposition is computed as

4.2 Low-Frequency Adaptation

In the context of DGSS, a stable scene content representation despite the style variance is important to predict the scene semantics. For each layer L i , the scene content from the frozen VFM features rests more in the low-frequency component f LL i (in Eq. 4), which is learned in the low-frequency adaptation branch.

Assume we have a low-frequency token T L i ∈ R m × c , where m is the sequence length of T L i , and c is the dimension of the frozen VFM feature defined in Sec. 3.1. The low-frequency token T L i is used to exploit the scene content from the low-frequency component f LL i , while at the same time following the low-rank adaptation paradigm (in Sec. 3.1).

First, we compute a similarity map S i ∈ R n × m between the token T L i and low-frequency component f LL i from the frozen VFM feature, which measures the correlation between each element in T L i and each patch embedding represented in f LL i , given by

where Softmax denotes the softmax activation function.

Then, we project the token feature T L i into the feature space of f LL i by a multilayer perceptron (MLP) parameterized by weight parameters W 1 i and bias parameters b 1 i , followed by the point-wise product with the similarity map S L i . The product with S L i allows the token features T i to better align to f LL i , where the scene content is highlighted. Assume the output is denoted as f LL i . Briefly, this process can be mathematically expressed as

Then, we fuse the projected token features f LL i with the low-frequency features f LL i by another MLP parameterized by weight parameters W 2 i and bias parameters b 2 i , followed by the skip connection. Assuming the output is ˜ f LL i , this process can be mathematically computed as

4.3 High-Frequency Adaptation

For DGSS, the robustness to the cross-domain style variance is particularly important. Such style difference is usually reflected on the high-frequency components. The Haar wavelet transform

enables the separation of these high-frequency components f LH i , f HL i and f i HH (in Eq. 4). Directly eliminating all the high-frequency components seems to be a simple and straight-forward solution, but it also leads to the loss of other information such as structure and object boundary. It may degrade a scene representation and decline the segmentation performance. Therefore, the objective of the high-frequency adaptation branch is to mitigate the impact of cross-domain style variation, not directly removing all the high-frequency components.

As the decoupling of styles does not differentiate whether the high-frequency components are from the horizontal, vertical or diagonal directions, for simplicity, we concatenate them together for processing in this branch. Specifically, still in layer L i , assume we have a high-frequency token T i H ∈ R 3 m × c . The token size of T i H is tripled compared with the token size of T L i , as three highfrequency components are involved. Similar to the low-frequency branch, the high-frequency token T i H is used to exploit the style information from the high-frequency components f LH i , f HL i and f i HH , while at the same time following the low-rank adaptation paradigm (in Sec. 3.1).

Same as the low-frequency adaptation branch, we compute a similarity map S H i ∈ R n × m between the token T i H and high-frequency components f LH i , f HL i and f i HH , given by

where [ · , · ] denotes the concatenation operation.

Then, the highlighted positions in S H i reveal the predominant style responses from the source domain images. The high responses, which reflect more domain-specific styles, are supposed to be suppressed during training. It allows the fine-tuned VFM features to be less impacted by the domain-specific styles. Instance normalization [55, 31], which computes the channel-wise mean and standard deviation, is effective to eliminate the styles. To this end, an instance normalization is implemented on the feature-token similarity map S H i , given by

Then, we project the token feature T i H into the high-frequency feature space by a multilayer perceptron (MLP) parameterized by weight parameters W 3 i and bias parameters b 3 i , followed by the point-wise product with the similarity map S H i . The product with S H i allows the token features T i H to better align to the decoupled high-frequency features, which is less relevant to the source domain. Assume the output is denoted as f H i . Briefly, this process can be mathematically expressed as

Afterwards, we fuse the projected token features f H i with the high-frequency features f i H by another MLP parameterized by weight parameters W 4 i and bias parameters b 4 i , followed by the skip connection. Assuming the outputs of these three components are ˜ f LH i , ˜ f HL i and ˜ f i HH , this process can be mathematically computed as

Finally, the low-frequency component ˜ f LL i and high-frequency components ˜ f LH i , ˜ f HL i , ˜ f i HH that have been processed by both branches are fused and transferred back by the inverse Haar wavelet transform. The output, denoted as f i +1 , is the input of the next frozen layer L i +1 .

4.4 Implementation Details

Same as the REIN [69] baseline, the loss function L of FADA directly inherits the losses from the Mask2Former decoder [12], given by

where L ce , L dice and L cls denote the cross-entropy loss, dice loss and classification loss. Here the hyper-parameters λ ce , λ dice and L cls are 5.0, 5.0 and 2.0, respectively.

By default we use DINO-V2 [53] as the frozen VFM, but the proposed FADA is also feasible to other VFMs. For fair evaluation, the Mask2Former segmentation decoder [13] is used to generate the pixel-wise prediction as REIN does. Same as the existing paradigm [69], the images are re-sized to 512 × 512 pixels before input to the models. The Adam optimizer with an initial learning rate of 1 × 10 -4 is used to train the model. The training process terminates after 20 epochs.

5 Experiments

5.1 Datasets & Evaluation Protocols

Five driving-scene semantic segmentation datasets that share 19 common scene categories are used for validation. Specifically, CityScapes (C) [16] consists of 2,975 and 500 images for training and validation, respectively. The images are captured under the clear conditions in tens of Germany cities. BDD-100K (B) [76] has 7,000 and 1,000 images for training and validation, respectively. The images are captured under diverse conditions from a variety of global cities. Mapillary (M) [51] is another large-scale semantic segmentation dataset, which consists of 25,000 images from diverse conditions. SYNTHIA (S) [64] is a synthetic driving-scene segmentation dataset, which has 9,400 images. GTA5 (G) [63] is another synthetic dataset, which has 24,966 simulated images from the American street landscape.

Following the evaluation protocol of existing DGSS methods [55, 56, 14, 58], a certain dataset is used as the source domain for training and the rest four are used as unseen target domains for validation. Three commonly-used evaluation settings are: 1) G → C, B, M, S; 2) S → C, B, M, G; and 3) C → B, M, G, S. The evaluation metric is mean Intersection of Union (mIoU, in percentage %). All of our experiments are implemented and averaged by three independent repetitions, starting from different random seeds.

5.2 Comparison with State-of-the-art DGSS Methods

Existing DGSS methods are involved for comparison: 1) ResNet based methods, namely, IBN [55], IW [56], Iternorm [31], DRPC [78], ISW [15], GTR [57], DIRL [71], SHADE [82], SAW [58], WildNet [41], AdvStyle [83] and SPC [32]; 2) Mask2Former based methods, namely, HGFormer [19] and CMFormer [8]; 3) VFM based methods, namely, DIDEX [52] and REIN [69]. By default, the performance is directly cited from prior works [55, 56, 14, 58]. '-' denotes that the authors did not reported the results nor provided source code. '*' denotes re-implementation with official source code under all default settings.

GTA5 Source Domain. From left to right, the third column of Table 1 reports the performance. Compared with the VFM based REIN [69], the proposed FADA shows an mIoU improvement of 1.83%, 1.54%, 1.99% and 1.50% on the C, B, M and S target domains, respectively. In addition, the proposed FADA shows an average mIoU improvement of 20% and 10% when compared with ResNet and Mask2Former based DGSS methods, respectively.

SYNTHIA Source Domain. The fourth column of Table 1 shows that the proposed FADA achieves the state-of-the-art performance, outperforming the REIN by 1.45%, 1.41%, 1.22% and 1.29% mIoU on the C, B, M and G unseen target domains, respectively. In addition, the proposed FADA shows an average mIoU improvement of 15% and 6% over ResNet and Mask2Former based DGSS methods.

CityScapes Source Domain. The last column of Table 1 shows that the proposed FADA shows an mIoU improvement of 1.58%, 1.83%, 1.37% and 1.19% on the B, M, G and S unseen target domains, respectively. In addition, the proposed FADA shows an average mIoU improvement of 15% and 5% over existing ResNet and Mask2Former based methods, respectively.

5.3 Ablation Studies

On Each Haar Component. Five settings are involved for experiments: (1) No wavelet components are used. The model fine-tunes on the frozen VFM, which is a simplified version of REIN [69] removing the instance link module; (2) Only fine-tuning on the low-frequency component f LL i , and

best

,

second and

third , respectively.

directly fusing with three high-frequency components without fine-tuning; (3) fine-tuning on f LL i and f LH i ; (4) fine-tuning on f LL i , f LH i and f HL i ; (5) fine-tuning on all the four frequency components (Ours). Table 2 reports the results. Harnessing the low-frequency features leads to an average of 1% mIoU improvement on most experimental settings. In addition, fine-tuning on each high-frequency component leads to a slight improvement on unseen target domains. It demonstrates the necessity to decouple the impact of cross-domain styles.

On Low-Rank Dimension r . By default we set the low-rank dimension r as 16. We further test the generalization performance when r is 4, 8, 32 and 64, respectively. Table 3 reports the performance when CityScapes is used as the source domain. When r is 16 or 32, the performance on unseen target domains shows the most stable performance. However, when r is too small ( e . g ., 4 or 8) or too large

Baseline

Ours

Baseline

Ours

BDD100K

Mapillary

GTA5

SYNTHIA

( e . g ., 64), the performance on unseen target domains demonstrates a clear decline, which can be explained by the under-fitting and over-fitting, respectively.

Understanding from Cross-Domain Feature Correlation. We display the channel-wise correlation matrix of the last-layer feature embeddings from C source domain and B target domain. The results are displayed in Fig. 3. Brighter indicates higher response. FADA allows both low- and high-frequency token features from the source domain and unseen target domains to show similar channel-wise activation response, which allows the model to be better generalized to unseen target domains.

T-SNE Visualization. We display the features before the segmentation decoder by t-SNE visualization. The experiments are conducted under the C → B, M, G, S setting. The feature space of the original REIN and the proposed FADA is visualized in Fig. 4. The samples from different unseen target domains are more uniformly distributed by the proposed FADA, narrowing the domain gap.

Understanding the Benefit of Instance Normalization to Mitigate Domain-specific Information.

We extract the three high-frequency components from the last VFM layer, and display them by t-SNE visualization. The feature space without (denoted as w.o.) and with (denoted as with) implementing the instance normalization function is visualized in the first and second row of Fig. 5, respectively. The experiments are conducted under the C → B, M, G, S setting. It is observed that the implementation of instance normalization allows the samples from different unseen target domains to be more uniformly distributed, indicating its effectiveness to mitigate the domain-specific information containing in the high-frequency components.

5.4 Generalization on Other Settings

To Different VFMs. We test the translation ability of the proposed FADA to other VFMs, namely, CLIP [62], SAM [39] and EVA02 [20]. For comprehensive evaluation, each VFM is validated under full-training, fine-tuning (REIN), or frozen scheme [69], respectively. The reported one decimal results are directly cited from [69]. Experiments are conducted under the G → {C, B, M} setting. Table 4 shows the superiority of FADA when embedded into these VFMs. For comparison with parameter-efficient fine-tuning (PEFT) methods, please refer to Table 7 in the supplementary material.

To Adverse Domains Adverse Conditions Dataset with Correspondence (ACDC) [65] is a semantic segmentation dataset that consists of samples from four types of adverse conditions, namely, rain, fog, night and snow. Table 5 shows that the proposed FADA outperforms existing DGSS methods by up to 0.7%, 1.5%, 2.5% and 2.9% on the fog, night, rain and snow domains, respectively.

HH

HL

LH

w.o.

with

Unseen images

ISW

SAW

REIN

Ours

WildNet

SPC

Unseen images

ISW

SAW

REIN

Ours

WildNet

SPC

5.5 Quantitative Segmentation Results

Some exemplar segmentation results are compared under the C → B, M, G, S and C → adverse domains are provided in Fig. 6 and Fig. 7, respectively. The proposed FADA shows better pixel-wise prediction than not only ResNet based methods ( i . e ., ISW [14], SAW [58], WildNet [41], and SPC [32]) and Mask2Former based methods ( i . e ., CMFormer [8]), but also VFM based REIN [69].

6 Conclusion

In this paper, we focused on adapting VFM for DGSS by exploiting the style-invariant properties from the VFMs, and presented a novel F requencyADA pted learning scheme to push this frontier. Concisely, Haar wavelet transform was introduced to decouple the frozen VFM features into low- and high-frequency components, which contain more scene content and style information, respectively. We innovatively modified the low-rank adaptation paradigm to both frequency features, and alleviated the impact of cross-domain variation on high-frequency features. Consequently, the model achieved a better generalization on unseen target domains. Extensive experiments and ablation studies on a variety of settings showed the effectiveness of the proposed FADA.

Limitation Discussion & Broader Societal Impact. The proposed FADA handles the low- and highfrequency features separately, which increases the trainable parameters compared with prior work (Table 4). However, the increase of about 6M parameters is acceptable. The proposed FADA advances the reliability and safety of autonomous driving and alleviates human involvement, benefiting the human well-being. We do not envision any negative social impact.

Acknowledgments and Disclosure of Funding. This work was supported by the Science and Technology Major Project of Guangxi (AA22096030 and AA22096032), and National Key R&D Program of China under Grant (2020AAA0109500 and 2020AAA0109501).

References

Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer. Intrinsic dimensionality explains the effectiveness of language model fine-tuning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics , pages 7319-7328, 2021.

Woo-Jin Ahn, Geun-Yeong Yang, Hyun-Duck Choi, and Myo-Taeg Lim. Style blind domain generalized semantic segmentation via covariance alignment and semantic consistence contrastive learning. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition , 2024.

Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In Computer VisionECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9 , pages 404-417, 2006.

Qi Bi, Shaodi You, and Theo Gevers. Interactive learning of intrinsic and extrinsic properties for all-day semantic segmentation. IEEE Transactions on Image Processing , 32:3821-3835, 2023.

Qi Bi, Jingjun Yi, Hao Zheng, Wei Ji, Yawen Huang, Yuexiang Li, and Yefeng Zheng. Learning generalized medical image segmentation from decoupled feature queries. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 810-818, 2024.

Qi Bi, Shaodi You, and Theo Gevers. Generalized foggy-scene semantic segmentation by frequency decoupling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1389-1399, 2024.

Qi Bi, Shaodi You, and Theo Gevers. Learning generalized segmentation for foggy-scenes by bi-directional wavelet guidance. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 801-809, 2024.

Qi Bi, Shaodi You, and Theo Gevers. Learning content-enhanced mask transformer for domain generalized urban-scene segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 819-827, 2024.

Prithvijit Chattopadhyay, Kartik Sarangmath, Vivek Vijaykumar, and Judy Hoffman. Pasta: Proportional amplitude spectrum training augmentation for syn-to-real domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 19288-19300, 2023.

Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, and Ping Luo. Adaptformer: Adapting vision transformers for scalable visual recognition. Advances in Neural Information Processing Systems , 35:16664-16678, 2022.

Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 17653-17662, 2022.

Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems , 34:17864-17875, 2021.

Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Maskedattention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1290-1299, 2022.

S. Choi, S. Jung, H. Yun, J. Kim, S. Kim, and J. Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 11580-11590, 2021.

Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1158011590, 2021.

Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, UweFranke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 3213-3223, 2016.

Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, and Xinmei Tian. Moderately distributional exploration for domain generalization. In International Conference on Machine Learning , pages 6786-6817, 2023.

Carlos A Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, et al. Ithaca365: Dataset and driving perception under repeated and challenging weather conditions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 21383-21392, 2022.

Jian Ding, Nan Xue, Gui-Song Xia, Bernt Schiele, and Dengxin Dai. Hgformer: Hierarchical grouping transformer for domain generalized semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 15413-15423, 2023.

Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao. Eva: Exploring the limits of masked visual representation learning at scale. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 19358-19369, 2023.

Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe Wu, Liang Chen, and Jia Li. Parameterefficient fine-tuning with discrete fourier transform. In International Conference on Machine Learning , 2024.

Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych, and Evgeny Burnaev. Extremal domain translation with neural optimal transport. Advances in Neural Information Processing Systems , 36, 2023.

Ziyang Gong, Fuhao Li, Yupeng Deng, Wenjun Shen, Xianzheng Ma, Zhenming Ji, and Nan Xia. Train one, generalize to all: Generalizable semantic segmentation from single-scene to all adverse scenes. In Proceedings of the 31st ACM International Conference on Multimedia , pages 2275-2284, 2023.

Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xiangwei Zhu, and Zhenming Ji. Coda: Instructive chain-of-domain adaptation with severity-aware visual prompt tuning. arXiv preprint arXiv:2403.17369 , 2024.

Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, and Shuiwang Ji. Joint learning of label and environment causal independence for graph out-of-distribution generalization. Advances in Neural Information Processing Systems , 36, 2023.

Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter Staar, Shady Abu-Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, et al. Unsupervised domain generalization by learning a bridge across domains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5280-5290, 2022.

Conghui Hu and Gim Hee Lee. Feature representation learning for unsupervised cross-domain image retrieval. In European Conference on Computer Vision , pages 529-544. Springer, 2022.

Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations , 2021.

Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6891-6902, 2021.

L. Huang, Y. Zhou, F. Zhu, L. Liu, and L. Shao. Iterative normalization: Beyond standardization towards efficient whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4874-4883, 2019.

Lei Huang, Yi Zhou, Fan Zhu, Li Liu, and Ling Shao. Iterative normalization: Beyond standardization towards efficient whitening. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition , pages 4874-4883, 2019.

Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, and Zhiwei Xiong. Style projected clustering for domain generalized semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3061-3071, 2023.

Christoph Hümmer, Manuel Schwonberg, Liangwei Zhong, Hu Cao, Alois Knoll, and Hanno Gottschalk. Vltseg: Simple transfer of clip-based vision-language representations for domain generalized semantic segmentation. arXiv preprint arXiv:2312.02021 , 2023.

Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, et al. Calibrated rgb-d salient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 9471-9481, 2021.

Wei Ji, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Qi Bi, Jingjing Li, Hanruo Liu, Li Cheng, and Yefeng Zheng. Learning calibrated medical image segmentation via multi-rater agreement modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1234112351, 2021.

Wei Ji, Jingjing Li, Cheng Bian, Zongwei Zhou, Jiaying Zhao, Alan L Yuille, and Li Cheng. Multispectral video semantic segmentation: A benchmark dataset and baseline. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1094-1104, 2023.

Wei Ji, Jingjing Li, Qi Bi, Tingwei Liu, Wenbo Li, and Li Cheng. Segment anything is not always perfect: An investigation of sam on different real-world applications. Machine Intelligence Research , 21:617-630, 2024.

Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European Conference on Computer Vision , pages 709-727, 2022.

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643 , 2023.

Sangrok Lee, Jongseong Bae, and Ha Young Kim. Decompose, adjust, compose: Effective normalization by playing with frequency for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 11776-11785, 2023.

Suhyeon Lee, Hongje Seong, Seongwon Lee, and Euntai Kim. Wildnet: Learning domain generalized semantic segmentation from the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9936-9946, 2022.

Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski. Measuring the intrinsic dimension of objective landscapes. In International Conference on Learning Representations , 2018.

Fuhao Li, Ziyang Gong, Yupeng Deng, Xianzheng Ma, Renrui Zhang, Zhenming Ji, Xiangwei Zhu, and Hong Zhang. Parsing all adverse scenes: Severity-aware semantic segmentation with mask-enhanced cross-domain consistency. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 13483-13491, 2024.

Jingjing Li, Wei Ji, Qi Bi, Cheng Yan, Miao Zhang, Yongri Piao, Huchuan Lu, et al. Joint semantic mining for weakly supervised rgb-d salient object detection. Advances in Neural Information Processing Systems , 34:11945-11959, 2021.

Yumeng Li, Dan Zhang, Margret Keuper, and Anna Khoreva. Intra-source style augmentation for improved domain generalization. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 509-519, 2023.

Shiqi Lin, Zhizheng Zhang, Zhipeng Huang, Yan Lu, Cuiling Lan, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Amey Parulkar, et al. Deep frequency filtering for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 11797-11807, 2023.

Fangrui Lv, Jian Liang, Shuang Li, Bin Zang, Chi Harold Liu, Ziteng Wang, and Di Liu. Causality inspired representation learning for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 8046-8056, 2022.

Divyat Mahajan, Shruti Tople, and Amit Sharma. Domain generalization using causal matching. In International Conference on Machine Learning , pages 7313-7324. PMLR, 2021.

MJehanzeb Mirza, Marc Masana, Horst Possegger, and Horst Bischof. An efficient domain-incremental learning approach to drive in all weather conditions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3001-3011, 2022.

Vincent W Neo, Soydan Redif, John G McWhirter, Jennifer Pestana, Ian K Proudler, Stephan Weiss, and Patrick A Naylor. Polynomial eigenvalue decomposition for multichannel broadband signal processing: A mathematical technique offering new insights and solutions. IEEE Signal Processing Magazine , 40(7): 18-37, 2023.

Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder. The mapillary vistas dataset for semantic understanding of street scenes. In Proceedings of the IEEE international conference on computer vision , pages 4990-4999, 2017.

Joshua Niemeijer, Manuel Schwonberg, Jan-Aike Termöhlen, Nico M Schmidt, and Tim Fingscheidt. Generalization by adaptation: Diffusion-based domain extension for domain-generalized semantic segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 2830-2840, 2024.

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 , 2023.

Junwen Pan, Qi Bi, Yanzhan Yang, Pengfei Zhu, and Cheng Bian. Label-efficient hybrid-supervised learning for medical image segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 2026-2034, 2022.

X. Pan, P. Luo, J. Shi, and X. Tang. Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European Conference on Computer Vision , pages 464-479, 2018.

X. Pan, X. Zhan, J. Shi, X. Tang, and P. Luo. Switchable whitening for deep representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1863-1871, 2019.

Duo Peng, Yinjie Lei, Lingqiao Liu, Pingping Zhang, and Jun Liu. Global and local texture randomization for synthetic-to-real semantic segmentation. IEEE Transactions on Image Processing , 30:6594-6608, 2021.

Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, and Wen Li. Semantic-aware domain generalized segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2594-2605, 2022.

Xi Peng, Fengchun Qiao, and Long Zhao. Out-of-domain generalization from a single source: An uncertainty quantification approach. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022.

Piotr Porwik and Agnieszka Lisowska. The haar-wavelet transform in digital image processing: its status and achievements. Machine graphics and vision , 13(1/2):79-98, 2004.

Fengchun Qiao, Long Zhao, and Xi Peng. Learning to learn single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 12556-12565, 2020.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning , pages 8748-8763, 2021.

Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun. Playing for data: Ground truth from computer games. In European conference on computer vision , pages 102-118. Springer, 2016.

German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez. The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 3234-3243, 2016.

Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 10765-10775, 2021.

Mattia Segu, Alessio Tonioni, and Federico Tombari. Batch normalization embeddings for deep domain generalization. Pattern Recognition , 135:109115, 2023.

Hao Shen, Zhong-Qiu Zhao, Yulun Zhang, and Zhao Zhang. Mutual information-driven triple interaction network for efficient image dehazing. In Proceedings of the 31st ACM International Conference on Multimedia , pages 7-16, 2023.

Peifeng Tong, Wu Su, He Li, Jialin Ding, Zhan Haoxiang, and Song Xi Chen. Distribution free domain generalization. In International Conference on Machine Learning , pages 34369-34378, 2023.

Zhixiang Wei, Lin Chen, Yi Jin, Xiaoxiao Ma, Tianle Liu, Pengyang Lin, Ben Wang, Huaian Chen, and Jinjin Zheng. Stronger, fewer, & superior: Harnessing vision foundation models for domain generalized semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024.

Mixue Xie, Shuang Li, Rui Zhang, and Chi Harold Liu. Dirichlet-based uncertainty calibration for active domain adaptation. In The Eleventh International Conference on Learning Representations , 2023.

Qi Xu, Liang Yao, Zhengkai Jiang, Guannan Jiang, Wenqing Chu, Wenhui Han, Wei Zhang, Chengjie Wang, and Ying Tai. Dirl: Domain-invariant representation learning for generalizable semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 2884-2892, 2022.

Xin Yang, Wending Yan, Yuan Yuan, Michael Bi Mi, and Robby T Tan. Semantic segmentation in multiple adverse weather conditions with domain knowledge retention. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 6558-6566, 2024.

Yuedong Yang, Hung-Yueh Chiang, Guihong Li, Diana Marculescu, and Radu Marculescu. Efficient low-rank backpropagation for vision transformer adaptation. Advances in Neural Information Processing Systems , 36, 2024.

Qinghao Ye, Xiyue Shen, Yuan Gao, Zirui Wang, Qi Bi, Ping Li, and Guang Yang. Temporal cue guided video highlight detection with low-rank audio-visual fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 7950-7959, 2021.

Jingjun Yi, Qi Bi, Hao Zheng, Haolan Zhan, Wei Ji, Yawen Huang, Shaoxin Li, Yuexiang Li, Yefeng Zheng, and Feiyue Huang. Hallucinated style distillation for single domain generalization in medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention , pages 438-448, 2024.

Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687 , 2(5):6, 2018.

Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao. Source-free domain adaptation for real-world image dehazing. In Proceedings of the 30th ACM International Conference on Multimedia , pages 6645-6654, 2022.

X. Yue, Y. Zhang, S. Zhao, A. Sangiovanni-Vincentelli, K. Keutzer, and B. Gong. Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 2100-2110, 2019.

Zhongqi Yue, Qianru Sun, and Hanwang Zhang. Make the u in uda matter: Invariant consistency learning for unsupervised domain adaptation. Advances in Neural Information Processing Systems , 36, 2023.

Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao. Domain generalization via entropy regularization. Advances in Neural Information Processing Systems , 33:16096-16107, 2020.

Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Spider: A unified framework for context-dependent concept segmentation. In Proceedings of the 41st International Conference on Machine Learning , volume 235, pages 60906-60926, 2024.

Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, and Gim Hee Lee. Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In Proceedings of the European Conference on Computer Vision , pages 535-552. Springer, 2022.

Zhun Zhong, Yuyang Zhao, Gim Hee Lee, and Nicu Sebe. Adversarial style augmentation for domain generalized urban-scene segmentation. In Advances in Neural Information Processing Systems , 2022.

Ronghang Zhu, Xiang Yu, and Sheng Li. Semi-supervised single domain generalization with label-free adversarial data augmentation. Transactions on Machine Learning Research , 2023.

A Appendix / supplemental material

A.1 Theoretical Analysis on Haar Wavelets

The Haar transform [60] cross-multiplies a function with various shifts and stretches, which has been demonstrated to be effective in applications, such as signal and image processing. In other words, it is able to analyze the local aspects of a signal.

Definition 1. Haar Scaling Function. Given an input signal x , the Haar scaling function is mathematically defined as

Given the space of all functions of the form ∑ k ∈ Z a k ϕ ( x -k ) as V 0 , where k ∈ Z is an arbitrary integer, and a k ∈ R . As each element of V 0 is zero outside a bounded set, such a function a k ϕ ( x -k ) has finite or compact support .

Definition 2. Basis of the Step Function Space. Given an arbitrary nonnegative integer j ∈ Z + 0 , Let V j denote the step function space at the level j , which is spanned by the set

Theorem 1. (1) A function f ( x ) belongs to V 0 ⇐⇒ f (2 j x ) belongs to V j . (2) A function f ( x ) belongs to V j ⇐⇒ f (2 -j x ) belongs to V 0 .

Proof. (1) If f ( x ) ∈ V 0 , then we have f ( x ) = ∑ k ∈ Z a k ϕ ( x -k ) , where a k ∈ R . Then, f (2 j x ) = ∑ k ∈ Z a k ϕ (2 j x -k ) . It means f (2 j x ) ∈ V j . (2) The proof of (2) is similar.

Theorem 2. The set of functions { 2 j/ 2 ϕ (2 j x -k ) , k ∈ Z } is an orthonormal basis of V j .

̸

Proof. The norm of a certain basis 2 j/ 2 ϕ (2 j x -k ) is | 2 j/ 2 ϕ (2 j x -k ) | = 2 j/ 2 | ϕ (2 j x -k ) | = 2 j/ 2 · 1 2 j/ 2 =1. (2) For any two basis m and n ( m = n ), < 2 m/ 2 ϕ (2 m x -k ) , 2 n/ 2 ϕ (2 n x -k ) > = 2 m/ 2 · 2 n/ 2 · < ϕ (2 m x -k ) , ϕ (2 n x -k ) > = 0. Thus, the set of functions { 2 j/ 2 ϕ (2 j x -k ) , k ∈ Z } is an orthonormal basis of V j .

Definition 3. Haar Wavelet Function. The Haar wavelet is the function ψ ( x ) = ϕ (2 x ) -ϕ (2 x -1) .

Theorem 3. The Haar wavelet function ψ ( x ) ∈ V 1 , and is orthogonal to V 0 .

Lemma 1. Any function f 1 ( x ) = ∑ k ∈ Z a k ϕ (2 x -k ) ∈ V 1 , i.e., orthogonal to each ϕ ( x -l ) , l ∈ Z if and only if a 1 = -a 0 , a 3 = -a 2 , · · ·

Proof. Given ϕ ( x ) ∈ V 0 , if f 1 ( x ) = ∑ k ∈ Z a k ϕ (2 x -k ) ⊥ V 0 , then ∑ k ∈ Z a k ϕ (2 x -k ) ⊥ ϕ ( x ) . Therefore, f 1 ( x ) ⊥ V 0 if and only if < ∑ k ∈ Z a k ϕ (2 x -k ) , ϕ ( x ) > = 0 . As ϕ ( x ) = ϕ (2 x ) -ϕ (2 x -1) , we have

(15)

Thus, a 0 = -a 1 . Similarly, by inspecting < ∑ k ∈ Z a k ϕ (2 x -k ) , ϕ ( x -1) > = 0 , we have a 2 + a 3 = 0 , · · · .

Proof of Theorem 3. Based on Lemma 1 , we have

A.2 Impact on Token Length m

By default we set the token length m in the proposed FADA as 100. To study its impact on the unseen target domain, the scenarios when m are set 25, 50, 75, 125, 150 and 175 are tested. CityScapes is used as the source domain. The results on B, M, G and S unseen target domains are displayed in Fig. 8 a, b, c and d, respectively. It is observed that, the generalization performance when m ranges from 75 to 125 is relatively stable, while the performance when m is too small ( i . e ., 25, 50) or too large ( i . e ., 150, 175) shows a slight decline.

















m





































(a) C to B

















m





































(b) C to M

















m





































(c) C to G

















m





































(d) C to S

Table 6: Ablation study on the positions of the frequency adapters. GTA5 as the source domain. CityScapes, BDD and Mappilary are unseen target domains. Evaluation metric is mIoU in %. Top three results are highlighted as best , second and third , respectively.

A.3 Impact on the Position of the Frequency Adapter

The high-level idea of our intuition focuses on the frequency space. As existing LoRA based and side-adapter based methods implement the adaptation on each of the transformer layer, therefore we treat the frequency space as a whole, and embed it into each transformer layer.

Nevertheless, it would be meaningful to have an in-depth analysis on the learning behaviour from shallow to deep. To this end, apart from the REIN baseline [69] and the proposed FADA, we further provide two experiments, where the frequency adapter is attached in the first half seven layers (denoted as shallow) and the second half seven layers (denoted as deep), respectively. Results in Table 6 show that:

Using the frequency adapter on the first half layers (shallow) shows a slightly better performance than on the second half layers (deep). It may be explained that the shallower features contain more cross-domain styles, such as illumination, landscape and etc .

Using the frequency adapter on all layers (ours) achieves the best performance, indicating its effectiveness on all layers.

A.4 Comparison with Token Fine-Tuning Methods

We further compare the proposed method with some existing parameter-efficient fine-tuning (PEFT) methods, namely, AdvStyle [83], PASTA [9], GTR-LTR [57], LoRA [28], AdaptFormer [10], VPT [38] and REIN [69]. Following the setting in REIN [69], GTAV is used as the source domain. BDD, CityScapes and Map are used as unseen target domains. Results in Table 7 show that the proposed FADA outperforms these methods on all unseen target domains.

A.5 More Visual Results

Fig. 9 and Fig. 10 show more results under C → B, M, G, S and C → ACDC setting. On both settings, the segmentation results show that the proposed FADA shows better pixel-wise prediction than the compared DGSS methods, especially in terms of the completeness of objects.

Unseen images

WildNet

ISW

SAW

REIN

Ours

SPC

Unseen images

WildNet

ISW

SAW

REIN

Ours

SPC

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: This paper proposes to learn style-invariant property from vision foundation model from the frequency space by Haar wavelet transformation.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: At the end of the conclusion section, the limitation of the proposed method has been discussed.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: The theory assumptions are in the methodology section.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: The model realization and implementation details are provided in the experimental section.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: The datasets are public. The source code will be publicly available up on publication.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: The experimental settings and details are provided in the experiment section.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: The evaluation protocols of these segmentation datasets do not require report the error bars.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: The computation resources and details are discussed in experiment section.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The authors have read the code of ethics. The experiments are all on public datasets without obeying the code of ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: At the end of the conclusion section, the broader impacts have been discussed.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: This work does not have such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: The asserts used in this paper are all public available for academic researches.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: This paper does not release new assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

Peer reviews:
comments: SUMMARY:
This paper deals with adapting a computer vision foundational model to a new domain.  They propose to use the frequency based on Haar wavelets to  to decouple the style and content information and then to address separately  content and style  domain adaptation. Experiments show state of the art results.

STRENGTHS:
The paper is relatively well-written. Adapting high and low frequency components separately seems like a good idea although it could be more motivated.  

Figure 1 is compelling. 

Experimental results show consistent improvement. 

Tested across various foundational models.

WEAKNESSES:
The adaptation for low-frequency and high-frequency components are different. The paper could provide more intuition to explain the particular adaptations chosen for low and high frequency components.

QUESTIONS:
see above

LIMITATIONS:
Yes
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a Frequency-Adapted (FADA) learning scheme, where Haar wavelet transformation is introduced to decouple the frozen VFM features into low- and high-frequency components. Experiments demonstrate the proposed method achieves a better generalization on unseen target domains.\n\nSTRENGTHS:\nThe proposed method achieves good performance.\n\nWEAKNESSES:\n1. The writing and logic are poor. For example, the relationship among the existing three DGSS category methods is missing. Why explore the style-invariant properties of VFM is important and urgent? The introduction is not accompanied by an explanation of Figure 1.\n2. What are the advantages of 1) leveraging Haar wavelet for domain generalization and 2) enhancing the generalization ability of VFM features via frequency space in Domain Generalization by Frequency Decoupling over existing related works?\n3. [1] also focuses on mitigating the effects of style variations on the DGSS. What is the difference between the paper with [1]?\n[1] Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive Learning. CVPR 2024\n\nQUESTIONS:\nI'm more concerned about [1] also focusing on mitigating the effects of style variations on the DGSS. What is the difference between the paper with [1]? Besides, why is exploring the style-invariant properties of VFM important and urgent? What are the advantages of 1) leveraging Haar wavelet for domain generalization and 2) enhancing the generalization ability of VFM features via frequency space in Domain Generalization by Frequency Decoupling over existing related works?\n[1] Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive Learning. CVPR 2024\n\nLIMITATIONS:\nThe proposed method heavily relies on Low-/High-Frequency Decomposition. However, the manner is fixed and not adjusted dynamically.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-10"}

comments: SUMMARY:
This paper proposes a Frequency Adaptive (FADA) learning approach. Its core idea is to process content and style information separately, by using frequency tokens. Specifically, the FADA comprises two branches for low-frequency and high-frequency components. The high-frequency components learn scene styles and eliminate their impact on DGSS, while the lower-frequency components contribute to stabilizing scene contents. Experiments in various DGSS settings demonstrate FADA's performance and versatility across different VFMs.

STRENGTHS:
1. The overall presentation is pretty good.
2. The idea of using frequence token is impressive.
3. The achieved performance seems meaningful.

WEAKNESSES:
Honestly, I do not know well about this topic.
So I would like to ask some questions from the general perspective.

1. Is the assumption of "similar content yet varied style" practical? It seems that the main target of this paper is autonomous driving. Then it is rational to assume almost the same categorical distribution would be observed over various data, including the unseen test one. However, how about the joint distribution of content and style? Can we assume that they are independent across the dataset?

2. Can I see the results on the other VFMs? I understand that DINO-V2 is one of the most popular choices, but I am personally interested in whether the proposed method functions well on a more segmentation-oriented model, such as SAM. I think replacing DINO-V2 with the SAM image encoder can be an interesting experiment.

QUESTIONS:
See the weaknesses.

LIMITATIONS:
Yes.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-07-11

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel FADA learning scheme to improve domain-generalized semantic segmentation. The proposed method leverages the Haar wavelet transformation to separate style and content information into high- and low-frequency components, respectively, allowing for better handling of domain variations. Experimental results demonstrate that FADA outperforms existing DGSS methods, showcasing its effectiveness and versatility across various vision foundation models (VFMs).\n\nSTRENGTHS:\n1. The proposed method is interesting and show good DGSS performances on various DG settings, which will be beneficial to the community.\n\n2. Comprehensive ablation studies are done by the authors. The ablation on different VFMs is very important to showcase the model generalizability of the proposed low rank adaptation method.\n\n3. The method is clearly described and great visualizations are provided.\n\n\n----------------------------\n\nScore has been updated. Thanks the authors for the rebuttal.\n\nWEAKNESSES:\n1. Lack of comparison with other recent low rank adaptation methods during the experiments, e.g., the works as follows,\n\n[1] Gao, Z., Wang, Q., Chen, A., Liu, Z., Wu, B., Chen, L., & Li, J. (2024). Parameter-Efficient Fine-Tuning with Discrete Fourier Transform. arXiv preprint arXiv:2405.03003.\n[2] Yang, Y., Chiang, H. Y., Li, G., Marculescu, D., & Marculescu, R. (2024). Efficient low-rank backpropagation for vision transformer adaptation. Advances in Neural Information Processing Systems, 36.\n\n\n2. Lack of insights in the experimental analysis, e.g., in section 5.2, the authors simply listed out the performance gains brought by the proposed method compared with the state-of-art methods without mentioning why it can achieve these superior performances.\n\n3. The ground truth images are not listed on Figure 5 and Figure 6, which hinders the comparison regarding the qualitative performances.\n\n\n4.Lack of description of the implementation details. What losses are used during the experiments? Which lr scheduler is used?\n\nQUESTIONS:\n1. Can the proposed low rank adaptation method outperforms the other recent low rank adaptation methods as mentioned in weakness 1?\n\n\n2. The authors are encouraged to add more insights towards the proposed method in the experiment analysis section.\n\n3. The ground truth figures are suggested to be added in Figure 5 and Figure 6-\n\n4. The implementation details are encouraged to be enriched.\n\nLIMITATIONS:\nyes it is mentioned be the authors.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on adapting the vision foundation model for domain-generalized segmentation via frequency-aware adaptation. Specifically, the intermediate features are decomposed into low-frequency (content) and high-frequency (style) components, which are then being processed with separate low- and high- frequency branches. To this end, sufficient experiments on extensive datasets validate the effectiveness of the proposed method.\n\nSTRENGTHS:\nThe problem setting is important. To adapt the vision foundation model in a domain-generalizable manner, parameter-efficient fine-tuning such as LoRA has been extensively used, this paper proposed a solution in an orthogonal direction -- adapting in the frequency domain. The core intuition makes sense to me. This paper decomposes the features into low-frequency and high-frequency domains and is adapted separately. The whole solution is simple and elegant. Sufficient comparison on extensive datasets and difference foundation model architectures validates the effectiveness of the proposed method.\n\nWEAKNESSES:\n1. When conducting the adaptation, it is intuitive that the low-frequency/ high-frequency components correspond to the content/style. However, can the author comment on the intuition on adapting in all the transformer laters? It may not be that clear whether the low-frequency and high-frequency features in the later layers have such correspondence anymore. From that sense, it would be nice to ablate the layer location where the frequency adapter is added (as opposed to being added to all the layers) \n\n\n2. When comparing with the REIN, REIN only has a trainable parameter of 2.99M (compared to FADA 11.65), the author also shows in Table 3 that FADA performance is influenced by the number of Rank and tunable parameters. It would be nice to add an experiment that compares these two methods with similar tunable parameters. \n\n\n3. The core intuition behind the decomposition is to prioritize the domain-invariant low-frequency features (content features) over the high-frequency features. However, as indicated by the ablation results in Table 2, better outcomes were achieved by adapting to all features. The current model design does not differentiate between high-frequency and low-frequency features, raising questions about the alignment between the model design and the initial intuition. A potential experimental setup that might clarify this involves training the model on multiple domain datasets with a shared low-frequency branch and distinct high-frequency branches. However, this would alter the experimental conditions. I would appreciate the authors' comments on this matter.\n\n4. Conducing Haar transform can cause additional inference time, it would be nice to include the inference time comparision.\n\nQUESTIONS:\nSee the weakness section.\n\nLIMITATIONS:\nLimitations have been discussed.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper leverages the vision foundation model (VFM) for domain-generalized semantic segmentation (DGSS). It proposes to adapt the VFM to the downstream task in the frequency domain. The proposed method decouples the low-frequency and high-frequency components of the VFM features and separately applies low-rank adaptation to them. This aims to stabilize the domain-invariant content and, on the other hand, eliminate the impact of domain-specific styles on DGSS.\n\nSTRENGTHS:\n* The paper is well-written and organized. \n* The idea of applying low-rank adaptation of VFM in the frequency domain has some certain novelty. \n* The proposed method achieves competitive performance on several datasets across different settings compared to previous methods.\n\nWEAKNESSES:\n* **Insufficient justification of key concepts:** The paper's claim that domain-specific style and domain-invariant content can be demonstrated by high-frequency and low-frequency components is not well-justified in this context. And, the explanation of how the proposed method stabilizes domain-invariant content while mitigating domain-specific style is insufficiently supported.\n\n* **Lack of detailed analysis on style elimination:** Given the stated importance of style-invariant properties in VFM for DGSS representations, eliminating the impact of domain-specific styles is crucial. While the paper uses instance normalization to address this, it lacks a detailed analysis of the effectiveness of this approach.\n\n* **Unclear representation of Haar components:** Although the paper suggests that the three high-frequency components represent domain-specific style information that should be eliminated for better DGSS, Table 2 indicates that the best results are achieved by preserving all components. The discussion around this in Line 250 is not sufficiently informative.\n\n* **Inadequate comparison with previous methods:** The paper also mentions previous works that have utilized FFT for frequency analysis and claims that it may be the first to use Haar wavelet for domain generalization. However, it does not adequately discuss the advantages of Haar wavelet over other methods or provide comparative analysis in this context.\n\nQUESTIONS:\nVFMs are known for their strong generalization ability across different domains. What specific style-invariant properties of VFM are the authors attempting to exploit in this paper?\n\nLIMITATIONS:\nPlease provide the necessary evidence and adequate clarification of the problems in the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-30"}