DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Authors: Haochen Li
Rui Zhang
Hantao Yao
Xin Zhang
Yifan Hao
Xinkai Song
Xiaqing Li
Yongwei Zhao
Yunji Chen
Ling Li
Venue: NeurIPS 2024 poster (2024)
Keywords: Unsupervised domain adaptation; Object detection; Visual-language model
Primary area: machine_vision
TL;DR: We propose a novel Domain-Aware Adapter (DA-Ada) to exploit domain-invariant and domain-specific knowledge with the vision-language models for domain adaptive object detection.

Abstract: Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain.
As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD.
However, the domain-agnostic adapter is inevitably biased to the source domain.
It discards some beneficial knowledge discriminative on the unlabelled domain, \ie domain-specific knowledge of the target domain.
To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task.
The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge.
DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder.
Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection.
Our code is available at https://github.com/Therock90421/DA-Ada.

DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Haochen Li
Rui Zhang**
Hantao Yao
Xin Zhang²
Yifan Hao²
Xinkai Song² Xiaqing Li
Yongwei Zhao? Ling Li Yunji Chen
Intelligeett Software Research Center, Institute of Software, CAS, Beijing, China State Key Lab of Processors, Institute of Computing Technology. CAS, Beijing, China
3 State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS,
Beijing, China
University of Chinese Academy of Sciences, Beijing, China haochen2021@iscass cn, thangruißict. cn.cn, hantao www.nlpo.nnnpiiiimm awpp.i.aaa...........a.......... an. {zhangxin, haoyifan, songxinkai, lixiaqing, zhaoyongweil@ictt.ce ac. cn, lilingRiscas ac cyjeict. cn
Abstract
Domain adaptive object detection (DAOD) aims to generalize detectors trained on annotated source domain unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freez- ing the visual encoder and inserting domain-agnostic adapter can learn domain- invariant knowledge for DAOD. However. the domain- biased the source domain. discards some beneficial knowledge discriminative on the unlabelled domain, i. de.domain-speccie solve the issue, we propose novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariee the Domain-Invarianl Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://github.com htt.:e/ooo.oo..oooo/4////------
adapter is inevitably
knowledge of the target domain. To
knowledge. DA-Ada consists of
Introduction
Object detection 52 12 have achieved remarkable performance, but suffer severe perfor- mance drop when dealing with unseen data due to domain discrepancy. To alleviate this problem, domain adaptive object detection (DAOD) 7 is explored to transfer a detector trained on the labelled source domain to unlabelled target domain. Traditional DAOD works 7
68
82
65
37 71 25
generate the domain-aligned feature via fine-tuning the backbone, as depicted in Fig. aa). Neverthe- less, it easily biased towards the source domain since a considerable number of parameters need to be updated with the annotations only from the source domain.
visual- language models (VLMs) is widely used for two reasons: 1) few parameters need to be learned; 2) VLMs trained on large-scale image-text pairs extract highly generalized features. Recent works 57] have explored using prompt tuning to generate the domain-aware detection head for DAOD. However, they all extract the visual feature
Recently, applying prompt tuning [81 80 73
"Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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finetune
Brack Baee
Agowelic
Frack
Specilic
Only
012
-6.8
Brack Eats
ADD
ADD
Adeetee
Freora
62.
Oraek
Dantalv-aaare Afaperr
Dessatt-gganniic Adatter
Block
Block
Block
SLB
57.1
67.5
Vinual Knosder
Adapler
Visual Encoder
Late
52.6
Invuriant
Block Toput
Black Iupat
Black Iuput
Source
Fnrget Dessaln
(a) Traditional Methods (b) Demainn-gneetti Adapter
(c) Domsin-aware Adapter
(d) mAP(%) Comparison
Figure I: (a) Traditional DAOD methods optimize the backbone adversarially. (b) Domain-agnostie adapter is inserted into the frozen visual encoder to learn domain-invariin knowledge. (c) Domain- aware adapter can simultaneously capture the domain-specific knowledge from the discarded feature. (d) The mAp(%) comparison on the Cross-Weather Adaptation. Compared with original VLM, domain-agome -anainn- adapter brings significant improvement to the source domain but limited improve- ment to the source domain, while domain-aware adapter brings significant improvement to both source domain and target domain.
from the image with a frozen visual encoder, ignoring learning task-related knowledge and limiting the improvement of visual features' discriminative capabilities.
To inject the task-related knowledge into the visual encoder, some methods 24 insert an adapter module into the frozen backbone. Formally, a adapter shared across domains can be straightly introduced learn the task-related knowledge with the annotations of source domain and domain- aligned constraint, as shown in Fig. (b). However, this adapter is domain-agnostic and can only learn commain-invaraan knowledge between the two domains under the domain-alie aomain-aligned constraint. Besides, the domain-invariant knowledge is inevitably biased to the source domain, since the annotations are only from the source domain. As shown in Fig. [d). compared with the original VLM, the domain-agnosti adapter brings significant improvement to the source domain, while the improvement of the target domain is limited. Summarily, the bias of the -nnarinnnvariian knowledge learned from domain-agnostic adapter limits the generalization to the unseen target domain.
Trained on large-scale data, the VLM provides essential general knowledge on unseen images, while the learned Nomain--nvarian knowledge biased to the source domain shows limited improvement on the target domain. Consequently, when transferring essential general knowledge to the domain- invariant knowledge, the domain-agnose -ggnostic adapter discards some beneficial knowledge on the target domain. Basically, discards the domain-specifie knowledge that distinguishes the target domain but is different from the capture the target-specific knowledge from the discarded knowledge between the essential general knowledge and Nomain-invarian knowledge is effective way to boost the performance of the VLM DAOD task.
knowledge. In summary, adding a complementary adapter to
In this paper, we propose novel Domain- Adapter (DA-Ada) to facilitate the visual encoder learning the domain-specifie knowledge along with the domain-invariant knowledge. Formally, DA-Ada introduces a Nomain-Invariann Adapter (DIA) and Domain-Specific Adapter (DSA) to exploit
and -ppeiii knowledge, respectively, as shown in Fig. T().. The DIA is attached to the block of the visual encoder in parallel and optimized by aligning the feature distribution of two domains to learn between the input and output of the block to recover the domain-specifi knowledge discarded by the DIA. Since the difference represents the feature discarded by the block, the discarded knowledge between the essential general knowledge and difference. Hence, the DSA can regain the domain -speciff knowledge from the difference adaptively propose to improve the generalization ability target domain, shown in Fig. d)) Moreover, the Visual-guided Textual Adapter (VTA), embedding cross-domain information learned by DA-Ada into textual encoder to enhance the discriminability of detection head. Overall, the proposed DA-Ada can inject domain-invariane invariant and domain-specitf knowledge into VLM for DAOD.
knowledge. The DSA is fed with the difference
knowledge is also hidden in the
We conduct evaluations on mainstream DAOD benchmarks: Cross- Weather (Cityscapes > Foggy Cityscapes), Cross-Fov (KITTI- - Cityscapes), Rim-to-Real (SIM10K - Cityscapes) and Cross-Style (Pascal VOC - Clipart). Experimental results show that the proposed DA-Ada brings noticeable improvement and outperforms state-of-the-art methods by a large margin. For example, DA-Ada reaches 58.5% mAP on Cross- Cross-Weather, Weather, surpassing the state-of -tate-of-the-art are- DA-Pro 31 by 2.7%.

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2 Related Work
Visual-Languague models Visual-language models (VLMs) [50
33 34
embed visual and text
modalities into shared space, enabling cross modal alignment. Pre-trained with astonishing scale of image-text pairs, they demonstrate comprehensive visual understanding. CLIP [50] simultaneously trains visual encoder and textual encoder with 400 million image- text pairs, showing promising performance on both the seen and unseen classes. Furthermore, [79 19 14 61] distill the knowledge from the visual encoder of CLIP into the detection backbone and transform the textual encoder into
detection head. Considering strong generalization, we apply RegionCIIP (79 as the detector.
Domain Adaptive Object Detection (DAOD) aims to adapt the object detector [52] trained on the labelled source domain to the unlabelled target domain. Previous approaches can be broadly divided into two orthogonal categories: feature alignment and semi-supervised learning. Feature alignment [45 76
76 aims align the feature distributions of the two domains with domain discriminators 77, to generate domain-invariant knowledge in three levels: image-level
46
72
56
15
47
30
65 40), instance-level 77 [3 and category-level 62
25
38
To prevent
knowledge unique to each domain from interfering with alignment, recent works [55] 1 32 [66] propose multiple extractors [42] 40
63
67
27 and discriminators [69] decouple the domain-
invariant and domain-specific knowledge. In parallel, semi-supervised learning strives to augment training data with style transfer 43
TO and pseudo label (59) 39 M. However, applying
existing DAOD method to VLM would overfit the model to the training data, compromising the generalization of pre-trained models. To preserve the pre-trained knowledge. we opt to freeze the VLM and devise a novel domain- aware adapter to facilitate cross-domain aduptation. Compared with existing decoupling methods that only use domain-invariane features for detection, our method adopts a secouupling-rffssion strategy. It adaptively modify domain-invariant features with domain-speciff features to enhance the discriminability on the target domain.
Tuning method for VLM Adapting pre-trained VLM downstream tasks via global finetuning
is prohibitively expensive and casily overfitted to training datasets. To solve this issue, prompt
tuning [8]] replaces the hand-crafted prompts with the learnable tokens for the textual encoder.
Conditions like categories 80 [74, human prior [73 and domain knowledge (31] are attached to
attain robust performance on new tasks. However, they freeze the visual encoder, preventing
from learning cross-domane information for DAOD.In In parallel, originated from Natural Language Processing (NLP)
21], adapter tuning inserts learnable small layers into the visual encoder so that the backbone can learn knowledge from new tasks. ViT-Adapter [8] and Conv- Adapter (5] are proposed to efficiently transfer pre-trained knowledge to zero or few-shot visual tasks.
17 integrates the adapter into the CL.IP model, and (58) further analyzes the components to be frozen or Icarnable. [48) combines self-supervised learning to enhance the ability to extract low-level features. Recent [70] explore injecting task-relate knowledge into segmentation model SAM [29]. However, tuning the adapter directly on both domains will bias towards the source domain and fails to distinguish comain-speciffi knowledge, leading to insufficient discrimination In this paper, we propose a novel domain-aware adapter that explicitly learns both Nomain-invariant and domain-specifh knowledge inject cross domain information into the visual encoder.
the target domain.
3 Methodology
In this section, we present a novel Domain-aware Adapter (DA-Ada) tailored for DAOD. DA-Ada employs adapter tuning to introduce both domain-specific and comain-invariann knowledge into VLM. It is worth noting that the proposed method can be attached to any CNN-based detectors as a plug-and-play module. Without loss of generality, we take vanilla Faster-RCNN (52) as example.
3.1 Overview
Inspired by adapter tuning, we can custom learnable adapters to inject cross-domain information into the visual encoder. Specifically, to enrich the extracted features with high domain generalization capabilities, an ideal adapter should satisfy conditions from the following two aspects. First, it can model the commonalities between the source and target domains, i. dnaai-iinnvaii knowledge. Second, it can adaptively supply the unique attributes of each domain, i. se.domiin-specific knowledge.
3

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Both Dontain
Soarce Ouly
Targel Only
Source Classifier
Frozem
Learnable
Learnable
Head
Proposal
Labelled Source data
Visual Encuder Block
Visual Encoder Block 2
Visual Encoder Block N
VIA
Textual
CLIP
Eneader
Target Proposal
Unlabelled Target data
DA-Ada
DA-Ado
DA-Ada N
Target Classifier
(a) Overview of DA-Ada
D Egu
Euc
DIA
hr-1
by
CLI
Tesinal
hi-1
(b) Domain-aware Adapter Module (DA-Ada)
(c) Visual-guided Textual Adapter (VTA)
Figure 2: Overview of the proposed (a) DA-Ada for DAOD and the architecture of (b) the i-th
dowain-aware adapter module (c) the visual- gouuldd textual adapter.
In this perspective, we design an effective Domain- Adapter (DA-Ada) consisting Domain- Invariant Adapter (DIA) and a Domain-Specific Adapter (DSA). As shown in Fig. []), given input image x, we split the visual encoder into N blocks { by feature resolutions (N = ResNet).
Then attach N blocks with DA-Ada modules {A:}
J in Fig. bb:
hg 8xx); h; A, (h, 1, (b;,,)))
where S denotes the stem layer. For the i-th DA-Ada module, first feed the i-th block's input h into the i-th DIA module A to extract the domain invariatt features h/- Then we attain the domain-specific features h from the subtraction of h; and h by the DSA module AS:
b = A/ +
h A (b;-1 h//)
After that, we fuse hf, h with spatial attention to output h; for i-th block:
h, h h- h
where- denotes the element-wise Hadamard product. With learnable adapters, we obtain visual embedding v by for subsequent detection. As the visual embedding contains sufficient cross- domain information, we propose the Visual-guided Textual Adapter (VTA), projecting the visual embedding to the textual encoder to enhance the discriminability of the detection head. As shown in Fig. c), the visual- guided textual adapter uses the visual embedding v v to infer textual embedding e e² on source and target domain, which is utilized for prediction. Overall, the proposed DA- can inject domain-invariant and sppeiffc knowledge into VLM to improve cross-domain generalization ability.
Domain-Invariant Adapter (DIA)
The DIA module is proposed to inject the domain-invariant knowledge into the visual encoder. As shown in Fig. b)). applies a bottleneck to learn multi-scale domain knowledge, and the output distribution is aligned between domains for extracting domain-invariant knowledge. Specifically, for the i-th block of the visual encoder, we first forward the input feature h, E xxxxxx to the i-th DIA and filter comain-irrelevan information with embedding block E
h E = = (h;
After that, the embedding h Rbxexxxx helpful for domain representation learning. Low channel-dimensional features have less information redundancy and are more suitable for domain adaptation than high-dimensional ones. Following this spirit, the embedding is encouraged to be down-projected to a low channel-dimensionn vector h E bxxxxhxw to extract domain-invariant

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knowledge and filter redundant information. Formally, a down projeetroection e is applied to reduce the dimension to r:
hー= = e (h)
(5)
Considering that the scale of objects varies between domains, we introduce M down-projectors [C with different receptive fields, enabling it to capture various spatial features across multiple scales. Specifically, the embedding h = [h, h .... b is first split up evenly in the channel dimension. Then, each partition resized different resolutions and down-projecte Therefore, the multi-scale version of Eq. 5 is expressed as:
h = [ep (2).)2(155 exr (h
Furthermore, the low-dimensional knowledge hf is encouraged to be mapped back to the original dimensional feature space and supplemented to the pre-trained features. Typically, we apply the dimension- -arisinnn- function e on h to extract domain-invarian knowledge for the visual encoder.
A (b,-)) el (h!").
(7)
where A/ (h;-1) Rxxcxix output of the i-th DIA, and will be summed with F! (1; attain Nomain-invariinn feature hf in Eq. (2) To ensure DIA learning expected to be well aligned between the two domains. Therefore, .N domain discriminator {2) J. is attached to each h! to calculate adversarial loss Ldia. We will introduce this loss in Sec 3.5
-nvariaat knowledge, the hf
With the combination of dimensional reduction-increase increase processes and the constraints of detection and adversarial loss, the DIA can extract domain-invarian features while reducing redundant features.
3.3 Domain-Specific Adapter (DSA)
Adapted with DIA, the essential general knowledge of the frozen VLM is transferred to domain- invariant knowledge. However, the knowledge learned only through the DIA is biased towards the source domain and appears less discriminative the target domain. Considering the high generalization of essential general knowledge of the frozen VLM, we attribute this problem to the fact that the DIA discards some domain-specift knowledge that is highly generalizable on the unlabelled target domain. Since the difference between the input and output of the block denotes the discarded feature, the discarded domain-specifie knowledge is also hidden in the difference. To this end, propose the DSA module to recover domain-specific knowledge from the difference.
After the DIA injects the knowledge unique to the target domain is discarded by the output h Therefore, we first obtain the feature h. discarded by the visual encoder block from the difference of the input h and h/:
knowledge into the visual encoder, the domain-speciffe
= 8 (h,-1 h()),
(8)
where 8 is similar with the embedding block 8f As domain-specifer knowledge is hidden in the discarded difference h,, a bottleneck architecture is employed for adaptive knowledge extraction:
h4 eD' (h,),
h = (h h/) et'² (h'))))
(10)
where eD' eti' follow the same configurations as e², et to perceive multi- domain-specing knowledge bottleneck manner.
Generally speaking, domain-invariant knowledge dominates the process of transferring essential general knowledge of the VLM, while domain-specific knowledge fine-tunes this process based on the characteristics of each domain. To this end, it is a more reasonable way to adaptively supplement domain-specific knowledge with the extracted h through pixel-level attention rather than straightforward addition. Therefore, the injection of the whole DA-Ada is written as Eq. 3
3.4 Visual-guided Textual Adapter (VTA)
With the domain-aware adapter to inject domain-invariant and domain-speciff knowledge, the extracted visual feature shows rich discriminability, which can also be used to improve detection
5

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Vectors
Testual
Visaal Vectors
Testual
Tearnabie
Eacoder
Laaraatle Trateal Dowilptinn
Vioniggutted
Tronzal
Adapeer
Source Image
Targer Image
Visual Encoder
Deteztion Hend
(Clas) [Bhox]
Source Image
Target
Visual Enceder
Detertion Hend
[Chass] [Rbos]
Image
(a) DA-Pro
(b) Visual-guided Textual Adapter
Figure Comparison between (a) DA-Pro and (b) Visual-guided textual adapter.
head. Therefore, introduce the VTA to exploit the cross-domain information contained in the visual features to enhance the textual encoder.
In order to fully exploit the domain-invariant and domain-specific knowledge extracted by the DA- Ada module, we equip two learnable components for VTA: the fomain-invariant textual adapter P (DITA) and the domain-specifee textual adapter P P" (DSTA) shown in Fig. 2(c). The DITA is shared across domains to encode visual domain-invariant knowledge into the input of the textual encoder, optimized by domain discriminator DP. The DSTA tailored to further supplement domain-specific knowledge for the source domain S and the target domain T. In practice, the structure of DITA and DSTA 3-layer MLP with a hidden dimension of 512, projecting visual embeddings into 8 tokens for the textual encoder. Formally, the VTA embeds visual information into the textual embedding.
eee =(((V)) (VV)) = J(P(v"), (v²),
PT"")
(11)
where v v². and e; denote the visual embedding from domain S,T, the i-th class and its textual description. T denotes the textual encoder. of and c." is the textual-level classifier embedding of i-th class from the source and target domains, respectively.
Our proposed VTA introduces discriminative visual features into the textual encoder, alleviating the problem of insufficient adaptation plain textual tuning. Existing methods (31] only tune learnable textual descriptions for detection head, as shown in Fig. al.. However, textual descrip- tions are insufficient to describe certain inter-domain differences, e-g., differences fields of view, leading to a limited ability to learn cross-domain information. Different from them, VTA analyses nomain-invarrinn and Nomain--speciff knowledge from visual features, inferring an image-conditional detection head with high discriminability, as shown in Fig. b).
3.5 Optimization Objective
We aim to insert the DA-Ada into the visual encoder to learn cross-domain information and further tune the prompt for discriminative textual representation with image conditions. On the one hand, introduce domain adversarial loss to the DIA and DITA to guide the learning of domain-invariant information. Formally, we obtain the output features h 7.S I.T for the source image x, and the h.
target image x, of each DIA. and minimize the adversarial loss:
Eiaa Σ Ex |D ) Ex, 11 1.T 1
(12)
And the dohain-shared DITA is expected to be aligned between domains:
Laita - [Ex |! (v )12 + Ex, | |D
(13)
where v T denotes the source and target visual embedding.
On the other hand, we learn taskerelated domain-specific knowledge in semi-supervised manner. For the source image, we calculate the cross-entropy for each visual embedding S with its annotations y. For the target v first obtain the prediction via the hand-crafted prompt "A photo of [class]" and filter out high-confidence pseudo labels y'. then minimize the cross-entropy as well:
Last Lv seS.)) Leel CVT eT .yy"),
(14)
where x denotes Matrix multiplication.

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Table 1: Comparison (%) with existing methods Cross-Fov Cityse-ss (K-+C and Sim-to-Ree fo-Real adaptation SIMIOK -iitsscpsss (S-C)). denotes CLIP based methods.
PrrssappesFooge Cityscapes (C-+F),
C >F
K
C
S C
Methods
Person
Rider
Car
Truck
Bus
Train
Motor
Bieycle
mAP
mAP
mAP
DA-Faster
29.2
40.4
43.4
19.7
38.3
28.5
23.7
32.7
32.0
41.9
38.2
SIGMA++
38
46.4
4
61.0
32.1
52.2
44.6
34.8
39.9
44.5
49.5
57.7
CIGAR
44
46.1
47.3
62.1
27.8
56.6
44.3
33.7
41.3
44.9
48.5
58.5
CSDA 16
46.6
46.3
63.1
28.1
56.3
53.7
33.1
39.1
45.8
48.6
57.8
HT II]
52.1
55.8
67.5
32.7
55.9
49.1
40.1
50.3
50.4
60.3
65.5
DA-UDA 84
46.9
53.3
64.5
38.9
61.0
48.5
42.6
54.2
50.6
60.3
58.1
56.3
64.2
38.5
45.5
55.1
54.3
50.9
NSA-UDA
3
50.3
67.7
37.4
57.4
46.9
47.3
$4.3
52.7
55.6
56.3
DA-Pro
55.4
62.9
70.9
40.3
63.4
54.0
42.3
58.0
55.9
61.4
62.9
DA-Ada(Ours)
57.8
65.1
71.3
43.1
64.0
58.6
48.8
58.7
58.5
66.7
67.3
Table 2: Comparison (%) with existing methods on Cross-Style adaptation task Pascal VOC yliparlipart. denotes
CLIP [50] based methods.
Methods
Acro
Bike
Bout
Cat
Chair
Cow
Table
Dog
Horse
Motor
Person
Plant
Sheep
Sofa
Tran
Tv
mAP
U
20
35.0
24.4
21.3
53.5
2.3
34.2
61.2
31.0
48.6
23,9
FGRR 4
30.8
52.1
35.1
32.4
42.2
62.8
42.6
21.4
42.8
58.6
33.5
20.8
37.2
81.4
66.2
50.3
21.5
29.3
58.2
47.0
43.3
UMT 10
39.6 39.6
32.4
35.0
45.1
61
48.4
7.5
46.0
67.6
21.4
29.5
48.2
75.9
70.5
56.7
25.9
28.9
39.4
43.6
44.1
SIGMA 37
55.4
54.9
54.3
46.6
23.0
44.7
65.6
23.0
22.0
45.0
58.6
TIA 78
42.2
66.0
36.9
37.3
43.7
71.
49.7
18.2
44,9
58.9
18.2
29.1
40.7
87
67.4
49.7
27.4
27.8
57.1
50.6
46.3
SIGMA+
36.3
54.6
40.1
31.6
58.0
60.4
46.2
33.6
44.4
66.2
25.7
25.3
44.4
64.8
55.4
36.2
38.6
54.1
59,3 46.7
CMT
60
39.8
56.3
39.7
60.4
35.0
56.0
7.1
60.1
60.4
35.8
67.8
84.5
80.1
55.5
32.8
42.3
38.2
47.0
DA-Ada(Ours)* 42.3 75.1 48.9
45.9 49.0 71.8 55.6 15.4 50.7 56.6 19,9 20.6 61.3 80.7 73.0 29.2 37.5 21.5 $2.5 52.9 48.0
Meanwhile, decouple domain-invariant and domain-specific knowledge, we maximize the distribu- tion discrepancy between DIA and DSA.
deee Σ Ex x.x ma (h! hy h -]],
(15)
where cos(a,
Ja b || b
is absolute value of cosine distance, a threshold.
With the help of domain classifiers, DIA and DITA are encouraged to contain more Nomain-invariann knowledge. By minimizing deee. the gap between DIA and DSA will be enlarged, which promotes DSA to extract more domain-specific knowledge. Overall, the optimization objective is:
L Lett Aaia + Adita daita + Adeohudee &e
(16)
where Frgg is the regression loss, and Adia, Adita Adee are balance ratios.
4 Experiment
4.1 Datasets and Implementation
We evaluate our method four benchmarks: Cross-Weathent Wewther((Ctysccapee Fooggy Cityscapes 54
Pooss-Fov(KITT [18] Ciityscppes), Min-to-Reel((SMMM (28 Cityscapes) and Cross- (Paseaa
VOC [3] Clipart (6)). Following [31] we adapt RegionCCLIP(ReNeet5550 (22) with Faster- RCNN
architecture as the baseline detector. We detail the datasets and implementation in Sec. 6. and 6.3 of the Appendix.
4.2 Comparison to SOTA methods
We present representative state-of-the-ar DAOD approaches for comparison, including feature alignment and cemi-supervised learning methods.
Cross- Weether Adaptation Scenario Table ( illustrates that the proposed DA-Ada surpasses SOTA DA-Pro 31 by remarkable margin of 2. 2.6%, achieving the highest mAP over eight classes of 58.5%. Compared with existing methods, DA-Ada significantly improves seven categories (i.e. person,
7

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Table 3: Comparison (%) of domain-aware Table Ablation studies (%) of domain-aware adapter on Cross- Weather adaptation.
adapter with standard adapter.
Adapter
Source-only
Homain-sgnostic Domain-sware
DIA
DSA
Edees
mAP
Gains
Cras. Wenther
50.4
53.8
$7.1
52.6
Cross-FoV
57.9
63.2
65.8
53.8
Sim con-to-Bee -e---o--eall
58.4
62.4
68.3
54.8
56.2
+2.2
+3.0
Cross-Style 38.3
46.4
57.1
44.5
Table 5: Ablation (%) insertion site of domain- awarc adapter on Cross- Weather adaptation.
Table 6: Ablation (%) input and injection operation of
domain-awe aware adapter on Cross- Weather adaptation.
Block Block Block 3
Block
mAP
Input of DIA Input of DSA
Injection Operation
mAP
53.6
9.
3.(b.1)
52.6
54.0
3. 1) (.h..))
54.8
54,6
b.-1
55.2
b (F..h..)))
h. +h,
56.2
56.9
Bi bf
bg +b?
56.7
57.7
Cross-Auemtion(h/ hf. hz))
+ b/..""
Table 7: Ablation (%) Bottleneck dimension of Table 8: Comparison (%) of VTA with plain textual
domain-aware aware adapter. denotes input dimension. tuning methods.
Beitlenock Dimension
MAP
Methods
C >F
Gains
K .C
Gains
DA-Ada
DA
DA-Ada
Hand-crafted Prompt
52.6
59.5
64
256
COOP XT
53.5
+0,9
60.7
+1.2
128
192
512
768
$7.1
56.8
DA-Pro 31
61.4
256*
1024*
VTA(Ours)
55.8
+3.2
62,9
+3.4
rider, car, truck, bus. train, and bicycle) ranging from 0.4% 5.3%. The superior performance shows the remarkable effectiveness of the DA-Ada in the cross-domain generalization ability.
FOV Adaptation Scenario Table I (K-CC) indicates noticeable 5.3% improvement on the SOTA DA-Pro [31 by the DA-Ada. As K adaptation faces more complicated shape confusion than C-F, it requests higher discriminability of the model. Therefore, the considerable enhancement validates that the DA-Ada can efficiently learn robust visual encoder.
Sim-to-Real Adaptation Scenario We report the experimental results on SIMIOK + Cityscapes benchmark in Table (S-C) The proposed DA-Ada achieves the best results of 67.3 67.3% mAP, outperforming the previous best entry HT [1]] 65.5% with 1.8%. The performance of DA-Ada is superior in the difficult adaptation task, which further demonstrates that our strategy is robust not only in appearance but also in more complex semantics adaptation tasks.
Cross-Style Adaptation Scenario Additionally, assess DA-Ada the more challenging Cross- Style adaptation, where the semantic hierarchy has broader domain gap. DA-Ada peaks with 48.0% 48.0% outperforming all the SOTA methods presented in Table demonstrating that injecting cross-domain information into the visual encoder could benefit the adaptation. Especially, DA-Ada exceeds all the compared methods on six categories (acroplane, bike, bird, boat, bus, and sheep), which verifies the method is effective under challenging domain shifts and in multi-class problem scenarios.
4.3 Ablation Studies
Standard Adapter vs. Domain-aware Adapter We first compare the performance of the domain- aware adapter with existing adapters, including source-only adapter and domain aagostic adapter. As shown in Table while the domain-agnostic adapter surpasses the source-only version by 3.4% 5.7% on four benchmarks, applying the domain-aware adapter further improves 2.4% ~ 3.3% mAP. We further explored the reasons for this advantage, shown in Fig d)) Compared with oracle, the domain agnossic adapter reaches similar performance on the source domain, but suffers severe performance drop of 3. 3.7% on the target domain, indicating that it is biased towards the source domain. While improving the source domain with 0.4%, our method reaches the oracle on the target domain. The superioir performance indicates the domain-aware adapter not only aligns domain-invaricn knowledge more accurately, but also utilizes nomain-speciftt knowledge to improve the detector's discriminative ability on the target domain.
Ablation for Domain-aware Adapter We conduct comprehensive ablation studies on each com- ponent of the proposed method in Table Only introducing DIA to the backbone attains an mAP of 53,8% and optimizing each DIA with an independent discriminator L increases 1.0%. This indicates that learning domain-invarian adapters transfer task-related source knowledge to the target
8

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Table 9: Ablation studies (%) of VTA Table 10: Comparison (%) computational efficiency on Cross-
on Cross- Weather adaptation.
Weather adaptation
DITA
Cass
DSTA
mAP
Goins
Method
Backbune Param (M) Leurnable Puram (M) mAP Abs, Gains
57.1
DSS
6G
29,812
29. 29.812
57.6
+0.5
CSDA
33.645
33.645
45.3
57.9
40.8
AT
30
39.225
18: 783
50.9
+7.9
58.5
+1.4
DA- [I
34.834
0.008
55.9
4.3.3
DA-Ada(Ours)
36.620
1.794
domain. Moreover, the DSA boosts the DIA by 1.4% and 2.3% with the help of dees showing that learning Nomaiin-speciffi knowledge improves the discrimination of the target detection head.
Insertion Site We explicitly study the insertion site of DA-Ada, as shown Table When single adapter is applied, inserting the DA-Ada in the shallow block achieves better performance, e. DA- Ada with block obtain 55.1%, %, surpassing all other insertion sites with block 2/3/4. And increasing the number of DA-Ada from 1 to 4 leads to steady improvements of 1.8% %, 0.8 0.8%, 0.8% respectively.
Input and Injection Operation We analyze different input features and injection operations of DIA/DSA in Table 6 Directly inserting DIA into the visual encoder and directly adding to the output of each block attains 2.2% improvement, showing the effectiveness of learning Nomain--nvarian knowledge. However, there is limited performance gain in sending the output F, (b, to DSA. It indicates that domain-specific knowledge is ignored during feature extraction of the visual encoder. To this end, inputting h - (h;-1) to DSA receives 56.2% exhibiting that the DSA can regain the domain-specifie knowledge from the difference. As h = A! (h,-1) (h,-1) is updated to be domain-invariinn, h (ha)) removes domain-invariant parts and appears to be domain-specifer Therefore, we forward it to DSA and gain an improvement of 0.5%, demonstrating the efficacy of learning domain -seeiif knowledge. Additionally, we substitute cross attention and livel-level attention for the direct addition, and gains highest mAP of 57.0% and 57. 57.1%. It reveals that domain- specific knowledge describes intra-domain properties and more suitable for refining the extracted features. For efficiency, we adopt the simpler pixel-level attention as the fusion function.
Bottleneck Dimension We also conduct ablation study in Table to explore the optimal bottleneck dimension of the DA-Ada. As the dimension increases, the performance peaks 57.1% when the bottleneck dimension is 1/2 of the input and then appears to decline. We conclude that appropriate dimensional reduction can filter redundant features while extracting task knowledge.
Textual Tuning Visual-guided Textual Adapter We compare the risual-guided textual adapter against existing methods, as shown in Table [8] Guided by visual conditions, VTA outperforms SOTA plain textual tuning methods by margins of 0.7% and 1.5% in two scenarios. Notably, VTA excels in the challenging Cross-FoV adaptation, suggesting that the visual modality effectively supplements the limitations of the textual encoder in describing domain attributes.
Ablation for Visual-guided Textual Adapter As shown in Table 9 learning DITA attains an mAP of 57.6%, and by introducing an additional adversarial loss, it achieves 57.9%. Moreover, with DSTA generating prompts for each domain, it exhibits a full adaptation performance of 58.5%. This shows that embedding image conditions into textual encoder can promote cross-domain detection.
Computational Efficiency As shown in Table I0 employing VLM yields a similar parameter scale while achieving a peak mAP of 58.5%. This indicates that the superior performance of DA-Ada does not arise from an increase in parameters. Furthermore, DA-Ada achieves the highest absolute gain of +8.0% with the training of only 1.794M parameters, demonstrating remarkable efficiency.
4.4 Detection Visualization
In Fig. we present the comparison of the ground truth boxes (a) and the detection boxes of SOTA DA-Pro (C) and our methods (b) on the target domain. (a. 1)C ()..))(c.((d)) (d.. are zoomed from the same region of images (c) for a better view. Fig. 4 1) presents eight objects in the cropped region: 6 overlapped cars and a rider with a bicycle. The baseline model only detects two clear cars in Fig. [b.1). Falling to describe domain information, like weather conditions, it misses other objects hidden in the fog. In Fig. C)., the DA-Pro distinguishes the rider and the bicycle and improves 9.3% mAP with the domain-adaptive prompt. However, it ignores one car on the left of Fig. 1).. suffering limited generalization ability due to insufficient domain representation learning in the visual encoder. Our proposed Ada correctly detects the missing car (labelled in green) in the cropped region Fig. 4 d.). By injecting cross-domain information into the visual encoder,

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(n.1)
(c.1)
(6) DA-Pro[311]
mAP:67.5
Baseline
(b.1)
(d.1)
(d)
DA-Adn (Ours)
MAP: 54.6
Figure 4: Detection comparison on the Cross- Weather adaptation scenario. We visualize the ground truth (a), the detection boxes of SOTA DA-Pro (c) and our methods (b)(). mAP: mean Average Precision on the example image
(a) Target Image
(b) Traitional Adapter
(6) DIA
(d) DSA
(c) DA-Ada
Figure 5: Feature comparison on the Cross- Weather adaptation scenario. We visualize (a) the target image and the output feature of (b) traditional adapter, (b) domain- invariant adapter (DIA), (c) domain-specific adapter (DSA) and (d) domain-aware adapter (DA-Ada).
the DA-Ada enables the model to detect more confidently on two bicycles (83%, 98%) and one person (91 %), compared with DA-Pro's (79%, 95%) and (89%). These comparison results reveal the effectiveness of DA-Ada.
4.5 Feature Visualization
In Fig. 5, visualize the output features of the traditional adapter, the domain-invariant adapter (DIA), domain-specific adapter (DSA) and the domain-aware adapter (DA-Ada). We sample image (a) and person in the fog from Foggy Cityscapes. The traditional adapter (b) roughly extracts the outline of the car. However, affected by target domain attributes, such as fog, background areas also highlighted in (b), and the person
salient. DIA (c) mainly focuses on the object area and extracts domain-shared task information. DSA (d) mainly focuses on factors related to domain attributes besides the objects, such foggy areas. By combining DIA with DSA, DA-Ada (e) extracts the car and person while reducing the interference of fog in the background. Compared with (b), objects are more salient in (e), indicating the effectiveness DA-Ada.
5 Conclusion
In this paper, we propose a novel Domain- Aware Adapter (DA-Ada) for DAOD. As small learnable attachment, it transfers highly generalized knowledge the visual-language model provides to cross- domain information for DAOD. Precisely, it consists of a Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and Domain-Specific Adapter (DSA) for recovering the domain-specific knowledge from information discarded by the visual encoder. Extensive experiments over multiple DAOD tasks validate the effectiveness of DA-Ada in inferring a discriminative detector.
Acknowledgments and Disclosure of Funding
This work is partially supported by the National Key R&D Program of China(under Grant FB4322245)2000) the NSF of China(under Grants 92364202, 61925208, U22A2028, 62222214, 62341411, 62102398. 62102399, U20A20227, 62372436, 62302478, 62302482, 62302483, 62302480, 62376268), 2022 Fundamental Disciplines Top Quality Student Training Program Project, Strategic Priority Research Program of the Chinese Academy Sciences, (Grant No. XDBB66000000 XDB0660201, XDB0660202), Major Program of ISCAS(Grant No. ISCAS-ZD- 202402), Beijing Natural Science (oundation(4222033), CAS Project for Young Scientists in Basic
Research (BBncchYYBR-02))) Youth Innovation Promotion Association CAS and Xplore Prize.
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Appendix
6.1 Datasets
Woss--eather Cityscapes [9] contains diverse street scenes captured by mobile camera in daylight. The regular partition consists of 2,975 training and 500 validation images annotated with eight classes. Foggy Cityscapes [54] simulates three distinct densities of fog on Cityscapes, containing 8,925 training images and 1,500 validation images. A standard configuration for cross-weathe adaptation take the training set of Cityscapes the source domain and the training of foggy Cityscapes as the target domain, evaluating cross-weather adaptation performance on the 1500-sized validation set in all eight categories.
Cross-Fot KITTI [8] is a crucial dataset for self-driving that includes 7,481 photos annotated with cars. Collected by driving in rural areas and on highways, it provides data with different Field of View (FoV). To fairly compare with other methods, we migrate KITTI to Cityscapes solely on the car category.
Sim-to-Real The synthetic dataset SIM10K 28 has 10,000 photos from the Grand Theft Auto video game with labelled bounding boxes in the class car. We follow existing works to perform this sim-to-real adaptation and report the performance on the class car.
Cross-Style Pascal VOC [13 is a widely-used real-world dataset containing 2007 and 2012 subsets, annotated with 20 classes. Clipart [26] is collected from the website with 1000 comical images, providing bounding box annotations with same classes as Pascal VOC. Following the mainstream splitting, we use Pascal VOC 2007 and 2012 train-val split with a total of 16,551 images as the source domain and all Clipart images as the target domain.
6.2 Implementation Details
Following 31]. we adapt RegionCLIP(ResNees. (22]) with domain classifier 7 as the baseline. We use the Faster-RCNN [52] as the detector with the default configurations. Following (7]. one batch of source images with ground truth and one batch of target domain images are forwarded to the proposed DA-Ada in each iteration to calculate the detection, adversarial and decoupling loss. The hyperparameter Adta Aditas Adec is set to 0.1. 1.0 and 0.1, respectively. We set the batch size of each domain to and use the SGD optimizer with a warm-up learning rate. Mean Average Precision (mAP) with threshold of 0.5 is taken the evaluation metric. All experiments are deployed on 8 Tesla V100 GPUs.
6.3 Comparison to SOTA methods
We present representative statc-of-the-a DAOD approaches for comparison, including feature alignment and semi-supervised learning methods.
Weass- Adaptation Scenario Table 1T (C-F) illustrates that the proposed DA-Ada sur- passes SOTA DA-Pro [31 by remarkable margin of 2.6%, achieving the highest mAP over eight classes of 58.5%. Compared with existing methods, our method significantly improves seven cate- gories (f.e. person, rider, car, truck, bus, train, and bicycle) ranging from 0.4% to 5.3%. Compared with the SOTA decoupling method D2-UDA [84), the DA-Ada attains an improvement of 7.9% and promotes 3.0 10.9% on all categories. The superior performance shows modifying domain- invariant knowledge with domain-specific knowledge could enhance the discriminative capability on the target domain.
Cross-FOV Adaptation Scenario Table II (K-C) indicates a noticeable 5.3% improvement on SOTA DA-Pro 31 by the DA-Ada, reaching astounding peak of 66. 66.7% mAP. As K-C adaptation faces more complicated shape confusion than C->F, it requests higher discriminability of the model. Therefore, the considerable enhancement validates that the proposed method can efficiently improve the discriminability of the visual encoder in new scenarios.
Cin-to-Real Adaptation Scenario We report the experimental results on SIM10K - CCtyscapes
benchmark Table I1 (S+C). The proposed DA-Ada achieves the best results of 67.3% mAP, outperforming the previous best entry HT [ 65.5% with 1.8%. The performance of DA-Ada is
15

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Table 11: Comparison (%) with existing methods Cross-Waes Weather adaptation Soyyscapes-Foggeee Cityscapes (C-F), Cross-Fov adaptation Cityaassss (K (K~CC) and Sim-to-Red Real adaptation SIMIOK -aie---iitsss Citysccppes (S-xC).
C-+F
K-+C
S-+
Methods
Person
Rider
Car
Truck
Bus
Train
Motor
Bicycle
mAP
MAP
mAP
DA-Faster
7
29.2
40.4
43.4
19.7
38.3
28.5
23.7
32.7
32.0
41.9
38.2
VDD
6x
33.4
44.0
51.7
31.9
52.0
14.7
34.2
36.8
40.0
DSS 65
42.9
51.2
53.6
33.6
49.2
18.9
36.2
41.8
40.9
42.7
44.5
MeGA
37.7
49.0
52.4
25.4
49.2
46.9
34.5
39.0
41.8
43.0
44.8
SCAN
20
41.7
43.9
57.3
28.7
48.6
48.7
31.0
37.3
42.1
45.8
52.6
TIA
78
52.1
38.1
49.7
37.7
34.8
46.3
48.6
31.1
42.3
44.0
DDF 22
37.6
45.5
56.1
30.7
50.4
47.0
31.1
39.8
42.3
46.0
44.3
SIGMA
44,0
43.9
60.3
31.6
50.4
51.5
31.7
40.6
44.2
45.8
53.7
SIGMA+
38
46.4
45.1
61.0
32.1
52.2
44.6
34,8
39,9
44.5
49.5
57.7
CIGAR
44.3
SAD 82
38.3
47.2
58.8
34,9
$7.7
48.3
35.7
42.0
45.2
49.2
47.8
CSDA 10
46.6
46.3
63.1
28.1
56.3
53.7
33.1
39.1
45.8
48.6
57.8
52.1
65.5
DR-
34
46.9
53.3
64.5
38.9
61.0
48.5
42.6
54.2
50.6
60.3
58.1
54.3
NSA-LDS LDA
S3
50.3
60.1
67.7
37.4
57.4
46.9
47.3
54.3
$2.7
55.6
56.3
70.9
54.0
DA MA-AAa(Qurr))
57.8
65,1
71.3
43.1
64.0
58.6
48.8
58.7
58.5(10.2)
66.7(
(..3(
Table 12: Comparison (%) with existing methods on Styss-Style adaptation task Pascal VOC-
Methods
Acro
Bike
Bird
Boat
Bondee
Bus
Car
Cat
Cow
Table
Dog
Horse
Motor
Porson
Plant
Sheap
Sofa
Train
TV
mAP
UaDAN
20
35.0 73.7
41.0
24.4
21.3
69.8
53.5
2.3
34.2
61.2
31.0
29.5
47.9
62.2
61.3
13.9
7.6
48.6
23.9
40.2
IED 66
27.9
64.8
28.4
29.5
25.7
64.2
47.7
13.5
47.5
50.9
50.8
21.3
33.9
60.2
65.6
42.5
15.1
40.5
45.5
48.6
41.2
DBGL 3
28.5
52.3
34.3
32.8
38.6
66.4
38.2
25.3
39.9
47.4
23,9
17.9
38.9
78.3
61
51.7
26
28.9
56.8
44.5
41.6
IPD 67
41.5
52.7
34.5
28
43.7
58.5
41.8
15.3
40.1
54.4
26.7
28.5
37.7
75.4 63.7
48.7
16.
30.8
54.5
48.7
42.1
FGRR 4
30.8
52.1 35.1
32.4
42.2
62,8
42.6
42.8
58.6
33.5
20.8
37.2
814
66.2
50.3
21.5
29.3
58.2
47.0
43.3
UMI
39.6
59.1 32.4
35.0
45.1
61.9
48.4
7.5
46.0
67.6
21.4
29.5
48.2
75.9
70.5
56.7
25.9
28,9
39.4
43.6
44.1
SIGMA
37
40.1 55.4 37.4
31.1
54.9
54.3
46.6
23.0
44.7
65.6
23.0
22.0
42.8
55.6
67.2
55.2
32.9
40.8
45.0
58.6
44.5
ATMT
35
37.5
63.4 37.9
29.8
45.1
62.7
41.2
19.5
43.7
57
22,9 25.3 39.6
87.1 70.9
50.6
29.1
32.2
58.4
50.5
45.2
CIGAR
44
35.2
55.0
39.2
30.7
58.1
46.9
47.0
61.0
21.8 26.7
44.6
52.4 68.5
54.4
31.3
38.8
56.5
63.5
46.2
TIA
78
42.2 66.0
36.9
37.3
43.7
71.8
49.7
18.2
44.9
58.9
18.2
29.
40.7
87.8
67
49.7
27
27.8
57.1
50.6
46.3
SIGMA++
31.6
33.6
66.2
44,4
58.8
36
38.6
46.7
CMT
39.8
56.3
38.7
39.7
60.4
35.0
56.0
7.1
60.1
60.4
35.8
28.
67.8
84.5
80.1
55.5
20.3
32.8
42.3
38.2
47.0
DA-Adu(Qurs)
80.7
48.0( (..I(
superior in the difficult adaptation task, which further demonstrates that our strategy is robust only in appearance but also in more complex semantics adaptation tasks.
Cross-Style Adaptation Scenario Additionally, assess DA-Ada the more challenging Cross- Style adaptation, where the semantic hierarchy has broader domain gap. DA-Ada peaks with 48.0%, outperforming all the SOTA methods presented in Table 12 It demonstrates that injecting cross-domain information into the visual encoder could benefit the adaptation. Especially, DA-Ada exceeds all the compared methods six categories (aeroplane, bike, bird, boat, bus, and sheep), which verifies the method is effective under challenging domain shifts and in multi-class problem scenarios.
6.4 Sensitivity Laia
Table 13: Sensitivity to parereparameters initialization of Adia-
Cityscapes Foggy CoyyyCitsscapes
Adia
0.01
0.05
0.1
0.5
1.0
10.0
mAP 57.1
57.8
58.5
58.1
58.0
53.4
To select hyper-parameters for the adversarial loss in DIA, we perform experiments of dif- ferent choices of the weight value Aaio.
We conduct the experiment on DA-Ada
16

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adaptation scenarios, shown in Table 13 Initialized with 0.01, the DIA suffers from insufficient learning of comain--nvvaiiann knowledge, only attaining 57.1% mAP. When initialized with 0.05 ~ 1.0, the performance of DA-Ada is similar and achieves the best of 58.5% with Adia 1.0. Increasing the Adia to 10.0 sufferers significant performance degradation. We attribute this to the model focusing too much alignment rather than detection.
Ciyseapes-FFogyCCiyye
6.5 Sensitivity on Adito
Table 14: Sensitivity to hyper-parameetrr' of initialization Laita.
Cityscapes- FoggyCiyyscpps
Adsta 0.1
0.5
1.0
5.0
10.0
MAP
58.1
58.2
58.5
57.9
56.9
We also explicitly study the sensitivity weight value Xdita for the adversarial loss in visual-guided domain prompt, shown in Table 14] As the weight value increases, the performance peaks with Adsta 1.0 and then appears to decline. Since hand-crafted token "A photo of [CLASS]" provides solid prior, the learnable prompt better initialized in the early stages of training. Therefore, the proposed model is more robust to the Adita compared to Adia-
6.6 Sensitivity on Adec
Table 15: Sensitivity hyper-parameters of initialization of Ldee-
Cityscapes Cityyyapysss
Adec
0.01
0.05
0.1
0.5
1.0
mAP 57.5
57.9
58.5
58.3
58.4
We also evaluate the sensitivity of weight value Adec for the decouple loss between DIA and DSA, as shown Table 15 As the weight value increases, the performance rises rapidly until Adec = 1.0 and then declines smoothly. Ldec decouples DIA to be orthogonal to the features extracted by DSA. Therefore, applying the decoupling loss with the same scale as the adversarial loss can optimize the goal relatively stably.
and domain-speciff knowledge by driving
6.7 Ablation for Multi-scale Down-projector C.)
Table 16: Ablation for Different Resolution in Multi-scale Down-projector CD.
Cityscapes-+ Foggy PitgyCCiyscspes
Resolutions
mAP
1
2
1
16
57.6
V
$7.8
58.3
58.5
58.2
Table 16 shows the impact of the different resolutions in multi-scale down-projector CD., The results indicate that while introducing various resolutions contributes modeling multi-scale domain knowledge, inappropriate receptive fields may harm feature extraction performance. Concretely, we observed that the mAP on the target domain (Foggy Cityscapes) peaks when the number of down-projectors M is set to 4, and the scaling ratios are {1, 21 4
1 11, respectively. And further
applying 16 to CD results in slightly performance degradation. These experiments suggest that
17

---

applying single convolution introducing excessive distinct resolutions is unsuitable for learning domain knowledge, and the choice of resolution requires consideration of the difference scale between the source and target domains.
6.8 Image or Instance-level Visual-guided Textual Adapter?
We explore whether to apply the visual-guided textual adapter at the image or instance levels. In DITA and DSTA, we replace the proposal embedding with the entire image as visual input, and achieves 57.8% suffering performance drop of 0.7%. This indicates that instance-level alignment avoids the influence of background on learning domain knowledge in the foreground.
6.9 Evaluation on multiple Baselines
Table 17: Results of multiple Baselines on C-FF adaptation.
Baseline
w/o DA-Ada with DA-Ada Gains
DSS CLIP+Faster-RCNT
40.9
48.1
+7.2
42.8
52.6
+9.8
To properly evaluate the method, we introduce Da-Ada two weaker baselines in 17 We inject DA- Ada into DSS and attain 7.2% improvement on mAP, showing great efficiency in feature-alignment first adapt the classification model CLIP Faster-RCNN to build a vanilla VLM detector with 42.8% mAP. Then we freeze the backbone and attach DA-Ada to the detector, achieving 52.6% mAP with improvement of 9.8%. Experiments show that with weak baselines, the proposed DA-Ada shows competitiveness to SOTA methods.
methods. For further validation,
6.10 Performance and Computational Overhead
Table 18: Comparison on performance and computational overhead on C-F adaptation. Method
mAP Interence time(s)/iter Training time(s)/ite Total iter Mem (MB)
Global Fine-tune 53.6
0.40
2.67
25000
12977
DA-Pro 54.6
0.40
1.47
1000
4034
DA-Ada 58.5
0.42
1.61
2500
6046
To verify the effectiveness of DA-Ada, we compare the performance and computational overhead with global fine-tune and DA-Pro on C-F adaptation. We initial the three methods with the same VLM backbone. Global fine-tuning has the largest time and memory overhead but only achieves the lowest performance, indicating the limitations of traditional DAOD methods in optimizing VLM. Compared with global fine-tuning, DA-Pro significantly reduces time and memory overhead while improving performance. Furthermore, DA-Ada significantly improves mAP with 4.9% while only using 6% of the time and 47% of the memory, showing great efficiency adapting cross-domain information to VLM.
6.11 Failure Cases
We provide some examples of failure cases the Weass-eetther adaptation scenario in Fig 6 We visualize the ground truth (a)(b) and the detection boxes of DA-Ada (c)(d) (c.1), DA-Ada misses the car with its headlights in the fog. Since the source data Cityscapes is collected sunny days, few cars turned on their lights the training set. Therefore, DA-Ada missed such out-of-distribution data. In (d.1), DA-Ada misses the bicycle and person blocked by other foreground objects. Since occlusion causes great damage to semantics, this type of missed detection is widely seen in object detection methods.
6.12 Limitation
Though effective, the proposed DA-Ada specially designed for the domain adaptive object detection task, where labelled source domain and unlabelled target domain needed. Currently, the
18

---

Ground
(
(Ours)
(b)
Ground Truth
(b.1)
(d.1)
(d)
DA-Ada (Ours)
Figure 6: Examples of failure cases the Cross Weashee adaptation scenario. We visualize the ground truth (a)(b) and the detection boxes of DA-Ada (c)(d) (a. (c. are zoomed from corresponding region for better view.
method cannot deal with the setting of multiple source domains or no target domain. We plan to resolve these problems in our future research.
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Reviews:
[{"review_id": "faPNN25V3p", "reviewer": "Reviewer_DyVN", "summary": "This article proposes a Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. The DA-Ada framework consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show the effectiveness of DA-Ada.", "questions": "See weaknesses", "limitations": "Adequately addressed", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written.\n2. The proposed method achieves significant performance improvement compared to several baselines on commonly used benchmarks. The proposed method not only improves the detection performance on the target domain, but also achieves improvement on the source domain.", "weaknesses": "1. The DIA module is a sequence of operations involving mapping, dimensionality reduction, slicing, and dimensionality-raising. It is challenging to understand why such operations can extract domain-invariant knowledge. The author should provide a stronger explanation to clarify the rationale behind this design.\n2. In line 251 of the text, could the author elaborate on how the source-only adapter and the domain-agnostic adapter each function? This would help readers better understand.\n3. Could the author design a quantitative experiment to further visualize which features represent domain-specific knowledge? Additionally, could they compare the traditional adapter with the method proposed in this paper to further demonstrate the effectiveness of the proposed method? The AP metric alone is insufficient to fully explain the motivation behind this method.\n4. Since this paper focuses on adapter work, could the author further review the related work on VLM (Vision-Language Models) adapters in more detail? This would help readers better understand the contribution of this paper.\n5. The paper mentions that the domain-agnostic adapter easily causes the model to bias towards the source domain. The latest work [1] also addresses the issue of source bias. In the related work and experiments sections, the author should include a discussion and comparison with the latest work.\n6. During this training process, are the original CLIP network components (including the visual encoder and the text encoder) completely frozen, with only the adapter and VTA parts being trained?\n7. minor error: There are several places between lines 91 and 102 where spaces are missing between sentences.\n\nReference:\n[1] DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection[C]//Forty-first International Conference on Machine Learning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Comment:\n\nWe sincerely thank you for your comprehensive comments and constructive advice. \nWe are pleased to see our work being regarded as achieving significant improvement. \nWe will explain your concerns point by point.\n\nQ1: **The DIA module is a sequence of operations...explanation behind this design.** \n\nA1:\nThanks for your valuable concern. \n\nIn fact, it is the combination of dimensional reduction-increase processes with the constraints of detection and adversarial loss that can extract domain-invariant features while reducing redundant features.\nThe structure of DIA is motivated by bottleneck [2].\nBottleneck reduces the computational cost by dimensional reduction-increase, efficiently learning feature representation.\nIn this structure, when down-projecting the input into low-dimensional features, some redundant features are discarded; when mapping low-dimensional features back to the original dimension, the task-related features are retained with the constraint of task loss.\nIn DIA, we first down-project the input features into low-dimensional features h^L then up-project to high-dimensional features h^I, optimizing with the adversarial and detection loss.\nBesides, we introduce the multi-scale scheme by slicing channels into different receptive fields, enabling it to capture domain-invariant knowledge in various scales.\nExperiments show that the mAP peaks 57.1% when the bottleneck dimension is 1/2 of the input (Line 2 of Table 7 in the paper), indicating that appropriate dimensional reduction can filter redundant information while extracting domain-invariant knowledge.\nApplying the scaling ratios of {1, 1/2, 1/4, 1/8} achieves 58.5%, demonstrating that multi-scale convolution can help deal with the domain bias in object scales.\n\nQ2: **How the source-only adapter and domain-agnostic adapter each function?** \n\nA2: Thanks for the suggestion.\n\nThe source-only adapter denotes the traditional adapter fine-tuned on the source domain.\nThe domain-agnostic adapter is tuned with detection loss on source domain and adversarial loss on both domains, as shown in Fig.1(b).\nWe will supplement the description in the manuscript.\n\nQ3: **Quantitative experiment to visualize which features represent domain-specific knowledge?**\n\nA3: Thanks for your constructive suggestion.\n\nWe visualize the output features of the traditional adapter, the domain-invariant adapter (DIA), domain-specific adapter (DSA) and the domain-aware adapter (DA-Ada) in Figure 5 in the Global Rebuttal part.\nWe sample image (a) a car and a person in the fog from Foggy Cityscapes.\nThe traditional adapter (b) roughly extracts the outline of the car. \nHowever, affected by target domain attributes, such as fog, background areas are also highlighted in (b), and the person is not salient.\nDIA (C) mainly focuses on the object area and extracts domain-shared task information.\nDSA (d) mainly focuses on factors related to domain attributes besides the objects, such as foggy areas.\nBy combining DIA with DSA, DA-Ada (e) extracts the car and person while reducing the interference of fog in the background.\nCompared with (b), objects are more salient in (e), indicating the effectiveness of DA-Ada.\n\nQ4: **Further review the related work on VLM adapters**\n\nA4: Thanks for your suggestion.\n\nRecent works explore adapters to transfer pre-trained VLM to few-shot visual tasks.\n[3] and [4] propose adapters to introduce image-related inductive biases into Transformer and CNN network.\n[6] firstly integrates the adapter into the CLIP model, and [5] further analyzes the components to be frozen or learnable.\n[7] combines self-supervised learning to enhance the ability to extract low-level features.\nRecent [8] explore injecting task-related knowledge into high-resolution segmentation model SAM.\nHowever, they also face the source-biased problem when applied to DAOD task.\nTo handle this, DA-Ada explicitly learns both domain-invariant and domain-specific knowledge.\nWe will provide a more detailed description in related work.\n\nQ5: **Comparison with the latest work [1].** \n\nA5:\nAs a semi-supervised method, [1] transfers the source domain image into target domain, aiming to train a style-unbiased classifier.\nHowever, it requires a large amount of image pre-processing and three stages of training. \nAnd [1] can only handle domain bias in image style.\nIn contrast, DA-Ada freezes the backbone and introduces lightweight learnable adapters.\nIt does not require data generation and only tuning a very small number (1.794M) of parameters to achieve significant adaptation of +8.0%(Line 5 of Table 10 in the paper). \nMeanwhile, since DA-Ada adaptively learns cross-domain information from visual features, it appears to be robust in various adaptation scenarios.\nExperiments show that DA-Ada outperforms [1] on three benchmarks with 6.3\\~17.4% mAP, and 5.1\\~5.6% absolute performance gain, indicating that DA-Ada is more effective and can handle a broader range of adaptation scenarios.\n|Benchmark| DSA-DA|Abs. Gain|DA-Ada|Abs. Gain|\n|-|-|-|-|-|\n|C→F|52.2|+2.9|58.5|+8.0|\n|K→C|49.3|+1.6|66.7|+7.2|\n|S→C|52.5|+1.1|67.3|+6.5|\n\nQ6: **During training process, are the original network completely frozen?** \n\nA6:\nYes. Only the adapter parts are trained, and all other CLIP components are frozen.\n\nQ7: **minor error in lines 91 and 102**\n\nA7:Thanks for pointing that out. We will revise these typos.\n\n[1]DSD-DA:Distillation-based Source Debiasing for Domain Adaptive Object Detection\n\n[2]Deep residual learning for image recognition\n\n[3]Adapterfusion:Non-destructive task composition for transfer learning\n\n[4]Conv-adapter: Exploring parameter efficient transfer learning for convnets\n\n[5]VL-ADAPTER: Parameter-efficient transfer learning for vision-and-language tasks\n\n[6]Clip-adapter: Better vision-language models with feature adapters\n\n[7] SVL-adapter: Self-supervised adapter for vision-language pretrained models\n\n[8]Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "e71SW5skon", "reviewer": "Reviewer_nqrX", "summary": "This work focuses on domain adaptive object detection (DAOD) with the vision-language models. The core idea behind this paper is the frozen visual encoder with a domain-agnostic adapter only captures domain-invariant knowledge for DAOD. To this end, this paper proposes a novel Domain-Aware Adapter (DA-Ada) to capture the domain-invariant knowledge and domain-specific knowledge. The experimental results on multiple DAOD tasks show the proposed method clearly outperforms existing DAOD methods.", "questions": "1. Do we really need a source domain for adaptation when we already have a powerful vision-language detector that contains much general knowledge from large-scale data?", "limitations": "Nothing.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tDAOD is an important problem, especially, in the large vision-language era, we need to rethink and explore the new paradigm for DAOD.\n2.\tThe proposed method is reasonable, effective transfer performance should maintain both the transferability (domain-invariant) and discriminability (domain-specific) between source and target domains.\n3.\tThe experimental results over multiple benchmarks show the effectiveness of the proposed method. Extensive ablation studies have been conducted to investigate the proposed method.", "weaknesses": "1.\tAlthough the proposed method is reasonable, it still has limited novelty for the domain adaptation community. The domain-invariant and domain-specific knowledge have been exploited by many previous works. \n2.\tThe function of dec loss is to maximize the distribution discrepancy between DIA and DSA. Why is the cosine similarity calculated by $h_i^I$ and $h_i^I \\cdot h_i^S$ instead of $h_i^I$ and $h_i^I$.\n3.\tLack of details for VTA, what are the structures of DITA, DSTA, and DSTA. Besides, it should be added the text description c in Figure 2 (c), the CLIP Textual Encoder adopts both the projected visual feature and the textual feature for better clarity.\n4.\tThe author should add results of source-only baseline, i.e., RegionCLIP, for a fair comparison.\n5.\tThe Injection Operation has many ways in Table 6, can the authors provide some explanation between $h_i^I + h_i^S$ and $ h_i^I + h_i^I \\cdot h_i^S $\n6.\tIn line 184, the figure reference should be Fig.2(c) instead of Fig.3(b).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Comment:\n\nWe sincerely thank you for your comprehensive comments and constructive advice. \nWe are pleased to see our work being regarded as reasonable and effective. \nWe will explain your concerns point by point.\n\nQ1:\n**Although the proposed method is reasonable...the domain-invariant and domain-specific knowledge have been exploited.**\n\nA1:\nThanks for your concern. \n\nRecent works [50, 2, 1, 59, 60] propose multiple extractors[38, 36, 57, 61] and discriminators [63, 76] to decouple the domain-invariant and domain-specific knowledge, aiming to disentangle knowledge unique to each domain.\nHowever, on the one hand, they only use domain-invariant features for detection, ignoring the improvement of discriminability brought by the characteristics of each domain, *e.g.* domain-specific knowledge.\nOn the other hand, Applying existing DAOD method to VLM would overfit the model to the training data, compromising the generalization of pre-trained models.\nIn contrast, we take advantage of the generalization of VLM to assist in domain adaptation and propose a novel decoupling-refusion strategy. \nWhile preserving the pre-trained essential general knowledge, it adaptively modifies domain-invariant features with domain-specific features to enhance the discriminability on the target domain.\nExperiments show that DA-Ada surpasses the SOTA disentangling method [76] (Line 6 of Table 1 in the paper) 6.4\\~9.2\\% on three benchmarks, indicating that our method explores the relationship between domain-invariant and domain-specific knowledge from a novel and effective perspective.\n\nQ2: **The function of dec loss...Why is the cosine similarity calculated by h^I and h^I\\* h^S instead of h^I and h^I.**\n\nA2:\nThanks for your nice suggestion. \n\n$\\mathbf{h}^S$ is the output features of DSA block, expected to extract domain-specific knowledge.\nTo adaptively fuse the domain-specific with the domain-invariant knowledge, we explore two kinds of injection operation: directly adding $\\mathbf{h}^I+\\mathbf{h}^S$ and pixel-level attention$\\mathbf{h}^I+\\mathbf{h}^I\\cdot \\mathbf{h}^S$.\nWe find that domain-specific knowledge describes intra-domain properties and is more suitable for refining the extracted domain-invariant features.\nAs shown in Table 6 in the paper, $\\mathbf{h}^I+\\mathbf{h}^I\\cdot \\mathbf{h}^S$ achieves better performance than directly adding $\\mathbf{h}^I+ \\mathbf{h}^S$.\nTherefore, $\\mathbf{h}^S$ functions as pixel-level attention for $\\mathbf{h}^I$.\nIn this case, $\\mathbf{h}^I\\cdot \\mathbf{h}^S$ represents features refined by the domain-specific knowledge, and maximizing the cosine similarity between $\\mathbf{h}^I$ and $\\mathbf{h}^I\\cdot \\mathbf{h}^S$ could help further decoupling the domain-invariant and domain-specific knowledge.\nIn addition, if we maximize the cosine similarity between $\\mathbf{h}^I$ and $\\mathbf{h}^S$, then the term $\\mathbf{h}^I\\cdot \\mathbf{h}^S$ eventually converges to 0, which is meaningless for adaptation.\n\nQ3: **Lack of details for VTA.... Besides, it should be added the text description c in Figure 2 (c)....for better clarity.**\n\nA3: Thanks for raising this question.\nThe structure of DITA and DSTA is a 3-layer MLP with a hidden dimension of 512.\nThe DITA and DSTA project visual embeddings into 8 tokens for the textual encoder.\nWe will supplement the details of VTA in the manuscript.\nWe will also add the textual description c as an input to the textual encoder in Fig.2(c). \n\nQ4: **The author should add results of source-only baseline, i.e., RegionCLIP.**\n\nA4: Thanks for this point. \n\nThe zero-shot results of the baseline, *e.g.* RegionCLIP, is 52.6\\% mAP in Line 1 of Table 4,6 and 8 in the paper.\nFine-tuned with only the source domain data, the source-only baseline achieves 50.5\\% mAP, suffering performance degradation on the target domain.\nEquipped with domain-adaptive adapter and visual-guided textual adapter, our proposed method achieves the highest absolute gain of 8.0\\% (Line 5 of Table 10 in the paper) over the source-only baseline, demonstrating remarkable efficiency.\nWe will modify the corresponding description in the manuscript to make it clearer.\n\nQ5: **The Injection Operation...can the authors provide some explanation between h^I+h^S and h^I+h^I\\*h^S** \n\nA5: Thanks for the question.\n\n$\\mathbf{h}^I+ \\mathbf{h}^S$ denotes directly adding the output of DIA and DSA and taking it as the output feature for the domain-aware adapter.\n$\\mathbf{h}^I+\\mathbf{h}^I\\cdot \\mathbf{h}^S$ denotes that $\\mathbf{h}^S$ is applied as pixel-level attention, multiplied with $\\mathbf{h}^I$ element-wise and added with the original $\\mathbf{h}^I$.\nExperiment shows that $\\mathbf{h}^I+\\mathbf{h}^I\\cdot \\mathbf{h}^S$ outperforms $\\mathbf{h}^I+ \\mathbf{h}^S$ (Line 5 and 7 of Table 6 in the paper), revealing that $\\mathbf{h}^S$ describes intra-domain properties and is more suitable for refining the $\\mathbf{h}^I$, rather than directly adding.\n\nQ6:**In line 184, the figure reference should be Fig.2(c) instead of Fig.3(b).**\n\nA6: Thanks for this point. We will fix this typo in the manuscript.\n\nQ7: **Do we really need a source domain for adaptation when we already have a powerful vision-language detector that contains much general knowledge from large-scale data?** \n\nA6:\nThanks for the intriguing question.\n\nPre-trained VLMs learn powerful generalization through large-scale data. \nAlthough VLMs' zero-shot capabilities are strong, they benefit from the general knowledge they learn, which seems like common sense over a wide range of data. \nWhen deployed to downstream tasks, VLMs can show better performance combined with task-specific knowledge.\nTherefore, if there is a lack of credible labels for downstream tasks, a manually annotated dataset that is highly relevant to them can help VLMs migrate to downstream tasks properly, that is, source domain data. \nThat is to say, the existence of the source domain is to help VLMs better adapt to the target domain. \nIn this sense, the source domain is still needed.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hQWFjuuKex", "reviewer": "Reviewer_xAKd", "summary": "This paper presents a method to tackle the domain adaptive object detection (DAOD) task within the framework of visual-language models (VLM). The authors propose a Domain-Aware Adapter (DA-Ada) to enhance the visual encoder's ability to learn both domain-invariant and domain-specific features. DA-Ada consists of two components: the Domain-Invariant Adapter (DIA) and the Domain-Specific Adapter (DSA). The DIA learns domain-invariant features by aligning the feature distribution between the source and target domains, while the DSA recovers domain-specific knowledge from the differences between the input and output of the visual encoder blocks. Additionally, the Visual-guided Textual Adapter (VTA) embeds cross-domain information into the textual encoder to improve detection head discriminability. Experiments on various DAOD benchmarks indicate that DA-Ada improves performance compared to state-of-the-art methods.", "questions": "1. What are the regression losses used in DA-Ada? Are they the same as those used in [28]?\n2. The symbols of down-projectors in Eq.6 and Section 6.7 are different. Could you clarify this discrepancy? Additionally, for the down-up projections, which operate on both the channel dimension (Eq.5) and the spatial dimensions, a clearer explanation would be beneficial.\n3. Since DA-Ada modules are added to different visual encoder blocks, the claim in line 139 that “Low-dimensional features have less information redundancy…” seems inappropriate. In my opinion, “low-dimensional” features typically refer to features from the earlier layers. In Section 2, Eq. 5 seems more related to “channel dimension” condensation. Additionally, in “Bottleneck Dimension” in Section 4.3 (also Table 7), it would be helpful if the authors could directly indicate the input channel dimensions.\n4. It would be beneficial to add some visualizations for failure cases.\n\nSome minor problems:\n1.\tTypos in line 183-184?\n2.\tLegend missing in Figure 3 (the red symbols)", "limitations": "Yes, the limitations are discussed in Sec 6.11.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper is well-written and easy to follow. The motivation for explicitly learning additional domain-specific features to enhance cross-domain performance is clear and straightforward. The disentanglement of domain-invariant and domain-specific features proposed in the Domain-Specific Adapter, along with the regularization loss in Eq. 15, appears to be effective. The ablation study is comprehensive and detailed, and the experimental results indicate that the proposed model achieves significant performance improvements compared to other state-of-the-art methods across different domain shift scenarios.", "weaknesses": "Despite the paper’s strengths, certain aspects of the discussion could be further refined for precision and clarity:\n1. The performance of the source-only baseline in Table 3 outperforms (or achieves similar performance to) most of the non-VLM-based models in Table 1. For example, on the Cross-Weather domain shift, the source-only model achieves an mAP of 50.4, whereas methods like AT[35], which are backbone ImageNet-pretrained, show a performance of 50.9, and their source-only variants perform roughly >10 mAP lower. While using a VLM vision encoder to generate essential general knowledge is reasonable, it raises the question of whether this task should still be categorized as cross-domain (domain adaptive) learning or as transfer learning (or VLM-based domain adaptation). Additionally, it would be helpful to add a column in Tables 1 and 2 to indicate the pretraining.\n2. It would be beneficial if the authors could also provide the comparisons about the computational speed", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Comment:\n\nWe sincerely thank you for your comprehensive comments and constructive advice. \nWe are pleased to see our work being regarded as effective, and the motivation as clear and straightforward.\nWe will explain your concerns point by point.\n\nQ1: **The performance of the source-only baseline in Table 3 outperforms (or achieves similar performance to) most of the non-VLM-based models in Table 1..., it raises the question of whether this task should still be categorized as cross-domain ...to indicate the pretraining.**\n\nA1: Thanks for the interesting question.\n\nOur research focuses on injecting new domain knowledge into pre-trained VLMs, while protecting the generalization ability of the pre-trained knowledge.\nDespite using a strong VLM backbone, our method still achieves significant absolute performance gains. \nAs shown in Table 10 in the paper, our method achieves an 8.0\\% improvement on Cross-Weather adaptation task, surpassing the 7.9\\% of the SOTA method AT[35]. \nIn addition, to properly evaluate the method, we introduce DA-Ada to weaker non-VLM baseline DSS[59] in Table 17. \nWith DA-Ada, DSS achieves a competitive 48.1\\% mAP to SOTA methods and attains 7.2\\% improvement, indicating that the proposed DA-Ada performs well even under the weak non-VLM baseline.\nFor ease of understanding, we will also add the description of pre-training to Tables 1 and 2.\n\nDomain adaptation (DA) aims to transfer source domain knowledge to the target domain.\nCompared with the backbone used in non-VLM DAOD method, the VLM used in our method differs only in the pre-trained data.\nEssentially, we design the domain-aware adapter and visual-guided textual adapter to learn knowledge from the source domain and transfer it to the target domain, which follows the general definition of domain adaptation.\nSpecifically, DA-Ada analyses the source domain data to transfer the general knowledge of the VLM to downstream tasks, that is, the target domain. \nIn this case, the presence of VLM makes the problem like a combination of traditional DA and source-free DA.\nTherefore, it is also appropriate to list our method as a sub-problem of DA, such as VLM-based domain adaptation.\n\n\nQ2: **It would be beneficial if the authors could also provide the comparisons about the computational speed** \n\nA2: Thanks for your advice.\n\nWe have compared computational speed over global fine-tune, the SOTA method DA-Pro [28] and our proposed DA-Ada in Table 18 in the Appendix.\nWe initial the three methods with the same VLM backbone. \nOnly attaching lightweight adapters, our DA-Ada slightly introduces 0.02s extra time, accounting for 5\\% inference time but significantly improves the performance.\nGlobal fine-tuning has the largest training time overhead but only achieves the lowest performance, indicating the limitations of traditional DAOD methods in optimizing VLM.\nCompared with global fine-tuning, DA-Pro significantly reduces training time overhead while improving performance. \nFurthermore, DA-Ada significantly improves mAP with 4.9\\% while only using 6\\% of the time and 47\\% of the memory, showing great efficiency in adapting cross-domain information to VLM.\nWe will also add some computational time comparisons for non-VLM methods.\n| Method |mAP |Inference time(s)/iter |Training time(s)/iter |Total iter |\n|-|-|-|-|-|\n| Global Fine-tune |53.6 |0.40 |2.67 |25000 |\n| DA-Pro[28] |54.6 |0.40 |1.47 |1000 |\n| DA-Ada |58.5 |0.42 |1.61 |2500 |\n\nQ3: **What are the regression losses used in DA-Ada? Are they the same as those used in [28]?**\n\nA3: Thanks for your question. \n\nThe regression loss is the smooth L1 loss, which is the same regression loss used in the DA-Pro[28].\n\nQ4: **The symbols of down-projectors in Eq.6 and Section 6.7 are different...a clearer explanation would be beneficial.**\n\n\nA4: Thanks for pointing this out.\n\nThe symbol in Eq.6 and Section 6.7 refers to the same down-projector.\nTherefore, we will unify their descriptions and use the same font.\n\nFor the down projectors, Eq.5 operates only on the channel dimension.\nAs a multi-scale version, Eq.6 operates on both channel and spatial dimensions.\nFor the up projectors, Eq.7 operates only on the channel dimension.\nWe will provide these details in the manuscript.\n\nQ5: **“Low-dimensional features have less information redundancy…”...seems more related to “channel dimension” condensation...it would be helpful if the authors could directly indicate the input channel dimensions.** \n\nA5: Thanks for the valuable suggestion.\n\nThe \"low-dimensional features\" in Line 139 refers to the output of down-projection layer $\\mathscr{C}^{D}$ in the proposed adapter, which is an intermediate feature of the bottleneck structure.\nSince it represents a reduction in channel dimension, we will revise the \"low-dimensional\" into \"low channel-dimensional\" for better clarity.\nAs for the “Bottleneck Dimension,” the input channel dimension is equal to Line 4 of Table 7, \\ie 64, 256, 512, 1024 for the four DA-Ada blocks, respectively.\nWe will supplement the explaination the number of input channels in Section 4.3 and highlight them in Table 7.\n\nQ6: **It would be beneficial to add some visualizations for failure cases.** \n\nA6: Thanks for your suggestion.\n\nWe provide some examples of failure cases on the Cross-Weather adaptation scenario in Figure 6 in the Global Rebuttal Part.\nWe visualize the ground truth (a)(b) and the detection boxes of DA-Ada (c)(d).\nIn (c.1), DA-Ada misses the car with its headlights on in the fog.\nSince the source data Cityscapes is collected on sunny days, few cars turned on their lights in the training set. \nTherefore, DA-Ada missed such out-of-distribution data.\nIn (d.1), DA-Ada misses the bicycle and person blocked by other foreground objects.\nSince occlusion causes great damage to semantics, this type of missed detection is widely seen in object detection methods.\n\nQ7: **minor problems**\n\nA7: Thanks for pointing this out. \nWe will correct the typos and supplement the legend in Figure 3.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Aydzxaihil", "reviewer": "Reviewer_N5kf", "summary": "The paper presents a Domain-Aware Adapter (DA-Ada) for DAOD based on VLMs, which aims to improve the model performance when applied to an unlabeled target domain. DA-Ada incorporates two types of adapters, including a  Domain-Invariant Adapter (DIA) to learn domain-invariant knowledge and a Domain-Specific Adapter (DSA) for injecting domain-specific knowledge. It also includes a Visual-guided Textual Adapter (VTA) to encode the cross-domain visual feature into a textural encoder to enhance detection. Experiments across various tasks show that DA-Ada significantly outperforms existing methods, demonstrating its effectiveness in eliminating the domain gap.", "questions": "1. In Line 139, are there any theoretical proofs or experimental justifications to support the claim that low-dimensional features have less information redundancy and are more suitable for domain adaptation? How can you ensure that low-dimensional representation is domain invariant? I don't think that the feature with less information redundancy means domain invariant.\n\n2. For the Visual-guided textual adapter, this design seems to follow CoCoop [1], a generic technique for improving model learning. The difference needs to be clarified.\n\n3. I am confused about the domain-specific adapter. The authors use the residual parts of the visual features to represent the domain-specific information. Is there any proof or literature that can justify the correctness of this assumption?\n\n4. Can this method be applied to more advanced detectors, such as DETR and CenterNet v2?\n\n5. The paper writing and organization need to improve. The authors propose DITA and DSTA but don't provide any information about them in the abstract. Additionally, the method descriptions and Fig.2/3 are not well matched, leading to the reading difficulty.  For example, in Line 184, where are DITA and DSTA in Fig. 2(b)?\n\n[1] Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022). Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 16816-16825).\n\nConsidering the significant performance gain of the proposed method, I'm happy to turn to a positive score if concerns are addressed.", "limitations": "No potential negative societal impact.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method can significantly improve DAOD performance, surpassing state-of-the-art works by a large margin.\n2. The general idea of using an adaptor with VLMs to assist DAOD is moderately interesting.\n3. The method is simple yet effective.", "weaknesses": "1. Some claims are confusing and lack sufficient justifications. \n2. Some designs are similar to existing works and lack sufficient technical contributions.\n3. The method is only deployed on Faster RCNN. The adaptability to more advanced detectors is unknown.\n4. The paper writing and organization need to improve. \n\nDetails are in Questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Comments:\n\nWe sincerely thank you for the valuable comments. \nWe are encouraged to see that our work is recognized as moderately interesting and effective.\nWe will explain your concerns point by point.\n\nQ1: **In Line 139, ...low-dimensional features have less information redundancy and are more suitable for domain adaptation?**\n\nA1: Thanks for raising an important point.\n\nIn fact, it is the combination of dimensional-reduction and dimensional-increase processes with the constraint of task loss that can reduce information redundancy and is more suitable for domain adaptation, rather than the low-dimensional features themselves.\nIn line 139, the low-dimensional features refer to the output of the down-projection layer, which is an intermediate feature of the adapter's bottleneck structure.\nThe bottleneck structure is first proposed in [2].\nIt reduces the computational cost and number of parameters through dimensional reduction-increase, efficiently learning feature representation.\nIn this structure, low-dimensional features function as intermediate vectors: when down-projecting the input into low-dimensional features, some redundant information is discarded; when mapping low-dimensional features back to the original dimension, the task-related features are retained with the constraint of task loss.\nIn DIA, we first down-project the input features into low-dimensional features $h^L$ then up-project to high-dimensional features $h^I$, optimizing with the adversarial loss $L_{dia}$ and detection loss $L_{det}$.\nTherefore, by combining dimensional reduction-increase with the constraints of detection and adversarial loss, we enable DIA to extract domain-invariant features while reducing redundant features.\nExperiments show that the performance peaks 57.1% when the bottleneck dimension is 1/2 of the input (Line 2 of Table 7 in the paper), indicating that appropriate dimensional reduction can filter redundant features while extracting domain-invariant knowledge.\n\nQ2: **The difference between Visual-guided textual adapter (VTA) and CoCoop [1] needs to be clarified.**\n\nA2: Thanks for your nice suggestion.\n\nCoCoop[1] projects image features into input-conditional tokens to lessen sensitivity to class shift. \nHowever, it generates tokens with a meta-net shared across domains, *e.g.* domain-agnostic projector.\nIgnoring the differences between images from different domains, [1] shows limited ability to distinguish inter-domain commonalities and characteristics, which are essential for DAOD.\nIn contrast, VTA explicitly injects domain-invariant and domain-specific knowledge into tokens.  \nIt consists of a domain-invariant textual adapter (DITA) $P$ and domain-specific textual adapters (DSTA) $P^S, P^T$. \nDITA $P$ is shared between domains to encode visual domain-invariant knowledge into prompt tokens, specially optimized by an adversarial loss $L_{dita}$. \nThe DSTA $P^S$ and $P^T$ are independent across domains to learn domain-specific knowledge with detection loss $L_{det}$ on each domain, respectively.\nMoreover, a decoupling loss $L_{dec}$ is also designed to boost the DIA and DSA to learn cross-domain information.\nVTA increases the textual encoder's discriminability with cross-domain information and outperforms CoCoOp by 2.3%, 2.2% on C->F, K->C adaptation tasks (Table 8 in the paper).\nIt shows the superiority of our adapter over CoCoOp’s image-conditional prompt.\n\nQ3: **I am confused about...the residual parts... to represent the domain-specific information**\n\nA3:\nThanks for raising an important point.\n\n[3] has studied utilizing residual parts to disentangle domain-invariant and domain-specific representations.\nIt considers the difference between the input and domain-invariant features as the domain-specific parts.\nHowever, on the one hand, [3] uses the input and output of the whole backbone to make the difference.\nSince the output is performed with a lot more convolutions than the input, they differ greatly in semantic level, extracting inaccurate domain-specific knowledge.\nOn the other hand, [3] only use domain-invariant features for detection, ignoring the capability of domain-specific knowledge to improve the discrimination of detector.\nTo solve this, we introduce the novel domain-specific adapter (DSA).\nFirst, to ensure semantic-level consistency, the two features used for calculating the difference differ only by 3 convolutions.\nSecond, to ensure DSA learns domain-specific knowledge, we maximize the distribution discrepancy between DIA and DSA.\nMoreover, the DSA is adaptively fused with domain-invariant features to further enhance the discriminability.\nExperiments show that using residual parts can effectively extract domain-specific knowledge, and improves 1.5% over not using the residual parts (Line 3, 5 of Table 6 in paper).\nAnd DSA achieves an absolute gain of 4.5%, outperforming the 2.6% in [3].\nThese results show the effectiveness of the proposed domain-specific adapter.\n\n\nQ4: **Can this method be applied to more advanced detectors?**\n\nA4: Thanks for your concern.\n\nWe apply the proposed method to DETR.\nThe vanilla DETR baseline achieves 41.6% mAP on C->F with domain discriminators.\nEquipping the domain-aware adapter brings 5.2% gains, reaching 46.8% mAP. \nTo further verify the effectiveness, we also extend DA-Ada to DAPL[4] for DA Classification task.\nIt increases Accuracy from 74.5% to 77.1% on Office-Home, proving the generalization.\n\nQ5: **DITA and DSTA lack information in the abstract...in Line 184, where are DITA and DSTA in Fig. 2(b)?**\n\nA5:\nThanks for the suggestion.\nWe will supplement descriptions of DITA and DSTA in the abstract, and revise the description of Figures in the manuscript.\nFor example, the \"Fig.2(b)\" in Line 184 should be revised as \"Fig.2(c)\".\n\n[1]Conditional prompt learning for vision-language models\n\n[2]Deep residual learning for image recognition\n\n[3]Vector-decomposed disentanglement for domain-invariant object detection\n\n[4]Domain Adaptation via Prompt Learning", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find that:

1. The idea of learning domain-aware adapter is **moderately interesting** (Reviewer N5kf), **reasonable** (Reviewer nqrX) and the motivation is **clear and straightforward** (Reviewer xAKd).
2. This paper addresses an **important** problem in the large vision-language era and **explore the new paradigm for DAOD** (Reviewer nqrX).
3. The proposed method is **simple yet effective** (Reviewer N5kf, Reviewer xAKd, Reviewer nqrX), **significantly improves DAOD performance** (Reviewer N5kf, Reviewer DyVN, Reviewer xAKd), and **surpasses state-of-the-art works by a large margin**(Reviewer N5kf).
4. The paper is **well-written** (Reviewer DyVN, Reviewer xAKd), the experiments is **extensive** (Reviewer nqrX), **comprehensive** and **detailed** (Reviewer xAKd).

We will revise the manuscript according to the reviewers' comments. The main changes we made include:

1. We add more details and explanations about the low-dimensional features in DIA, the injection operation in DSA, and the structure of VTA.
2. We add discussions and comparisons with the latest research on prompt tuning, adapters, and representation disentangling.
3. We add quantitative experiments to explore the effectiveness of the proposed method. We provide feature visualization of the traditional adapter, the proposed domain-invariant adapter, domain-specific adapter and domain-aware adapter in Figure 5 in the attached PDF. We also analyze some failure cases in Figure 6.
4. We revise the details in Figures and Tables and fix some typos in the manuscript. We add the textual description c as an input to the textual encoder in Fig.2(c), and add legends in Fig.3. We indicate the pre-training scheme for the model in Table 1 and Table 2.

Next, we address each reviewer's detailed concerns point by point. We hope we have addressed all of your concerns. Thank you!

Meta-review: This paper presents a novel technique for domain adaptive object detection (DAOD) with the vision-language models. The assumption is that the visual-language models (VLMs) can provide essential general knowledge on unseen images. Therefore, the visual encoder is frozen, and a domain-agnostic adapter is inserted to acquire domain invariant knowledge for DAOD. To keep the domain-specific knowledge of the target domain, a novel technique called Domain-Aware Adapter (DA-Ada) is proposed in this paper.


Experimental results show that the proposed method achieves significant performance improvement as compared to previous methods. The performance improvements of 1 – 2 mAP points is significant in object detection. In addition, detailed ablation study is conducted to facilitate better understanding of each of the modules. Therefore, the proposed algorithm is worth sharing in the community. 

The reviewers’ scores are consistently positive.

Here is just a comment to improve this paper:
- The authors are advised to make the texts in the figures much larger, e.g., as large as those in the main texts.
Decision: Accept (poster)