Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection
Authors: Yu Li
Xingyu Qiu
Yuqian Fu
Jie Chen
Tianwen Qian
Xu Zheng
Danda Pani Paudel
Yanwei Fu
Xuanjing Huang
Luc Van Gool
Yu-Gang Jiang
Venue: NeurIPS 2025 poster ()
Keywords: Cross-Domain Few-Shot Object Detection;  RAG; Few-Shot Learning;
Primary area: applications

Abstract: Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to detect novel objects with only a handful of labeled samples from previously unseen domains. While data augmentation and generative methods have shown promise in few-shot learning, their effectiveness for CD-FSOD remains unclear due to the need for both visual realism and domain alignment. Existing strategies, such as copy-paste augmentation and text-to-image generation, often fail to preserve the correct object category or produce backgrounds coherent with the target domain, making them non-trivial to apply directly to CD-FSOD. To address these challenges, we propose Domain-RAG, a training-free, retrieval-guided compositional image generation framework tailored for CD-FSOD. Domain-RAG consists of three stages: domain-aware background retrieval, domain-guided background generation, and foreground-background composition. Specifically, the input image is first decomposed into foreground and background regions. We then retrieve semantically and stylistically similar images to guide a generative model in synthesizing a new background, conditioned on both the original and retrieved contexts. Finally, the preserved foreground is composed with the newly generated domain-aligned background to form the generated image. Without requiring any additional supervision or training, Domain-RAG produces high-quality, domain-consistent samples across diverse tasks, including CD-FSOD, remote sensing FSOD, and camouflaged FSOD. Extensive experiments show consistent improvements over strong baselines and establish new state-of-the-art results. Codes will be released upon acceptance.The source code and instructions are available at https://github.com/LiYu0524/Domain-RAG.

Domain-RAG:
Damain-RAG
Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection
Yu Li
Xingyu Qiu
Yuqian Fu
Tianwen Qian* Luc Van Gool PNSAIT, Sofia University "St. Kliment Ohridski"
Jie Chen Xuanjing Huang
Xu Zheng Yu-Gang Jiang'
Danda Pani Paudel
Yanwei Fu²
Fudan University
Fuzhou University
*East China Normal University "HKUST(GZ)
Abstract
Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to detect novel ob- jects with only a handful of labeled samples from previously unseen domains. While data augmentation and generative methods have shown promise in few-shot learning, their effectiveness for CD-FSOD remains unclear due to the need for both visual realism and domain alignment. Existing strategies, such copy-paste augmentation and text-to-image generation, often fail to preserve the correct ob- ject category produce backgrounds coherent with the target domain, making them non-trivial to apply directly to CD-FSOD. To address these challenges, we propose Domain-RAC. a training-free, retrieval-guided compositional image gen- eration framework tailored for CD-FSOD. Domain-RAG consists of three stages: domain- background retrieval, domain -unaied background generation, and brrrruundbbakgrounddd composition. Specifically, the input image is first decom- posed into foreground and background regions. We then retrieve semantically and stylistically similar images to guide generative model in synthesizing a new background, conditioned on both the original and retrieved contexts. Finally, the preserved foreground is composed with the newly generated domain-aligned back- ground to form the generated image. Without requiring any additional supervision training, Domain-RAG produces high-quality, domain-consistent samples across diverse tasks, including CD-FSOD, remote sensing FSOD, and camouflaged FSOD. Extensive experiments show consistent improvements over strong baselines and establish new state-of ooaitt--f--hhe--tt art results. The source code and instructions are available at https///ghhaabcomm/////22200000
Introduction
an emerging task derived from cross- domain few-shot learning (CD-FSL) 13) aims to tackle few-shot object detection (FSOD) across different domains. Unlike conventional FSOD 200, which assumes source and target data share similar distributions, CD-FSOD considers more realistic scenarios with significant domain shifts, for example, transferring from natural images to industrial anomaly images, remote sensing imagery, or underwater environments. By simultaneously involving the challenges of few-shot learning and domain shift, CD-FSOD poses significant challenges for existing detectors.
Cross-Domain Few-Shot Object Detection (CD-FSOD) 10]
"These authors have equal contributions. "Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Input Image
Simple Dasclline
Test-to -n--aagee Generution based Methods
Beguet lage Audggaen
Fortgnend
Fargrewa Mackgrmat
Vartous Domains
(a) Cupy paste
(b) Foreground Aug
(c) Background Aug
(d) Ours
(c) Iustration Domain RAA
"A plotoo of (ealsurey)
Caption of (bookground)
Retrieved Imagee Guiced
Given images from distinct novel domains, we compare generation results of baseline methods (a-c) and our approach (d), and illustrate the main pipeline of our Nomain-RACG (c).
Figure 1:
Due to the extreme scarcity of labeled data, e.g., as few as I or 5 annotated samples per category, a natural and intuitive solution is to leverage data augmentation to alleviate the data bottleneck. Although image augmentation and generation techniques have been extensively studied and shown effective in other few-shot learning tasks [6]] 27 22 33), it remains unclear whether they can produce high-quality training samples for CD-FSOD. Different from the few-shot classification or in-domain FSOD, this setting requires not only accurate object annotation but also strong domain consistency, which has not been tackled in prior data augmentation- based few-shot learning methods.
To generate training images for CD-FSOD, the most straightforward approach is copy-paste (Fig. (a)) While easy to implement, such images often lack realism and domain coherence. A more advanced strategy to build on recent generative models, particularly the trending eetttoo-magee generation, such as SDXL objects via text prompts, such as "a photo of category" (Fig. [b)). However, they might fail to preserve the object semantics when applied to novel categories and domains. Such a category shift is problematic for CD-FSOD, which has to tackle fine-grained objects and the domain gap. Other approaches generate diverse backgrounds (Fig. [c)), guided by text descriptions of the image. While this better preserves the foreground, purely textual descriptions often fall short of capturing precise domain characteristics and struggle ensure semantic and visual consistency between foreground and background. These limitations motivate us to develop new image generation framework capable of synthesizing visually coherent, domain-ah -olnin- training samples for CD-FSOD. Specifically, we aim to: @ preserve the original foreground object, @ generate diverse backgrounds that are both semantically and stylistically aligned with the query image and its domain, and & produce visually realistic images with valid annotations suitable for downstream detection training.
4 FLUX [21] Most existing methods in this area focus on synthesizing foreground
To that end, we propose Domain-RAG. retrieval-guidee guidedd compositional image generation, framework built upon the principle of fix the foreground, cdlppi the buckground. Leveraging the nature of object detection, Domain-RAS RAG begins by decomposing the target image into its foreground object and background, where the background is recovered by applying an inpainting model [49] to the object- masked region. Although simple in principle, this step critical for preserving the original object and its annotations, laying the foundation for controllable compositional generation. The core challenge then lies in generating a new background that is semantically and stylistically compatible with the foreground. Inspired by the paradigm of retrieval- -uggennte generation (RAG) (24). we inject structured visual priors into the generative process guide background synthesis. As illustrated in Fig. Me). Domain-RAG consists of the following three stages: 1) Domain-Aware Background Retrieval. We introduce image database (e.g., COCO 3T) containing diverse natural scenes, from which we retrieve candidate backgrounds that are semantically and stylistically similar to the inpainted background of the target image. Semantic similarity is computed using high-level visual features, while style similarity is measured via style-based descriptors [16). 2)
Background Generation. Rather than using the retrieved backgrounds directly, we feed them along with the target's inpainted background into a generative model to synthesize a new background that better reflects the visual characteristics of the target domain. To ensure compatibility with modern diffusion models, Redux (3 is applied to convert visual image cues into descriptive text prompts, enabling Background Composition. Finally,
direct use of text-to-image generation models. 3) the preserved foreground is seamlessly composed onto the synthesized, domain-aligned background using a mask-guided generative model. The resulting image maintains the original object while embedding it in realistic, domain-consistent context (Fig. [d). The entire Domain-RA pipeline is

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training free and can be directly integrated with existing detectors without any additional supervision or retraining, making particularly suitable for low-shot scenarios such as 1-shot CD-FSOD.
We validate Domain-RAG on three various tasks that address few-shot object detection with domain shifts: CD>FSOD,, remote sensing FSOD (RS-FSOD), and camouflaged FSOD. In all tasks, our method consistently improves a strong baseline by an average +7.3, +1.1, and +2.1 mAP under the lowest-shot setting, achieving new state-of-the-art (SOTA) performance. These results demonstrate its broad applicability and effectiveness across diverse domains.
Our main contributions cetrieval-guided compositional image generation framework for boosting cross-domain few-shot object detection. 2) Domain RAAG enables image generation that preserves the original foreground while synthesizing domain-aligned backgrounds, guided by semantically and stylistically similar retrieved examples. 3) We achieve consistent performance improvements and new state-of-the-ar results across a broad range of CD-FSOD, remote sensing FSOD, and camouflaged FSOD tasks.
follows: 1) We propose Domain-RAN RAGG, training-freee model-agnostic,
2 Related Works
Cross- Few-Shot Tasks. Few-shot learning across domains has been widely studied [3
550
11 64). but most works focus only on classification. The more realistic task of comss- few-shot object detection (CD-FSOD) [O, [9, which involves both recognizing and localizing objects, remains underexplored. Recent methods like CD-ViTO [TO] and ETS [2] address CD-FSOD. CD-VITO introduces the task with closed-source setting (COCO as the only source), while ETS uses more practical open-source setting [9] and leverages data augmentation via pretrained GroundingDINO (33. In this paper, we adopt the open-source setting and further improve augmentation using retrieval-guided compositional generation.
8
29
63
54
FSOD Beyond Domains. Beyond classic CD-FSOD tasks, many FSOD or detection problems also involve domain shifts, even if not explicitly labeled as cross-domain. Two notable examples are Remote Sensing FSOD (RS-FSOD) 34] and Camouflaged FSOD [39] RS-FSOD uses remote sensing images, which differ from natural scenes in color, perspective, and resolution, creating a clear domain gap. Camouflaged FSOD involves detecting objects that blend into their backgrounds- like fish underwater or animals in the wild posing challenges for generalization. We include both tasks to assess our method under diverse and difficult cross-domain scenarios.
Data Augmentation. Data augmentation is a key technique for the vision community. Traditional methods for object detection, like copy-paste [12], cropping, and color jittering [2], are simple but offer limited semantic variety. Recently, generative models -sspccillly text-to-image models like ControlNet (55), SDXL 43]. FLUX
21). and FLUX-Fill 22 have enabled more advanced
augmentations. Methods such as X-Paste (59 Lin et al. 30 and Zhang et al. (57) generate new
3) use text prompts to jointly create foregrounds and backgrounds. However, these methods typically rely on large amounts of in-domain data for training, which limits their adaptability to novel categories or unseen domains. In contrast, our Domain-RAA RAGG is training-frre free and leverages retrieved real-world images visual priors to generate domain-consistent samples, making it well-suited for CD-FSOD.
foregrounds to paste on diverse backgrounds, while others 40
56
55
Retrieval-Augmentee Generation in Vision. First introduced in NLP [24], ettrival-aaugmente generation (RAG) enhances outputs by incorporating relevant retrieved content external knowledge. Its strong performance has led to applications in vision tasks such as image captioning [45] 25).
visual question answering [32 [5]. and image generation [ 38 60). and pose estimation. However, current RAG-based image generation methods are aimed at open-ended synthesis and are not suited for object detection, particularly in cross-domain few-shot settings, where both domain alignment and object fidelity are crucial. To the best of our knowledge, we are the first to introduce a RAG-inspired, training-fres image generation framework specifically designed for CD-FSOD.
3 Proposed Method
Problem Setup. The CD-FSOD task aims to adapt an object detector from source domain Ds to target domain Dr, where the data distributions Ps and Pr differ. We use the few-shot setting, i.e. N N-way K-shot protocol to evaluate detection results in Dr. Specifically, a support set SNxK C Dr
3

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Training Free
Inpointing
Madd
FLUX
Generated Barkgronnd
Fis Foreground
Adapt Backgreund
PROMPT Embed
PRONIT Embed
Target Image
Redux
Redux
Initial
PRIDUPT Embedd
Image
Mask
Background
Generated
FLUX FILL
Database
Background
Domaln-Aware Background Retrieval
Domain-Guided Background Generation
Brregroind-Backgroann Compusition
Figure 2: Illustration of our Domain-RAG. Built on our principle of "fix the foreground, adapt the background", we first decompose image and process with three key modules: domain-awar aware back- ground retrieval, domain-guides background generation, and senerround-backgrooue composition.
provides K labeled examples per novel class, and a query set used for evaluation. We use the open-source setting introduced in the 1st CD-FSOD Challenge [9], which allows foundation models pretrained on large-scale data, to explore the potential foundation models CD-FSOD, Particularly, instead of pretraining on the support set S and evaluate the results on the query set Q. To mitigate the limited size of S, we augment each support instance with synthetic images, effectively expanding each class from K to K x (n 1) examples.
Dg, directly finetune pretrained detector (e.g.. GroundingDINO (33)
novel, training-free free, retrieval-guided compositional genera- tion framework that enhances support diversity by generating domain-aligned samples. To enable retrieval, we use COCO (31 as the database Diases serving Following the core principle of "fix the foreground, adapt the background", Domain-RAs RAG processes each support image x E S by first decomposing it into foreground object(s) and background. As shown in Fig. 2, the framework then proceeds through three key stages: 1) domain-aween aware background retrieval first obtains the inpainted background binat from and then retrieves G candidate back- grounds bye from Doose that are semantically and stylistically similar. 2) domain domain-guided background generation feeds each (binit; pair into generative model synthesize new domain-aligned background bdom- 3) foregrounddbackground composition finally produces n new images x+ by compositing the preserved foreground onto each ddom using mask-guided generative model.
Overview.
We propose Domain-RACE
gallery of candidate backgrounds.
3.1 Domain-Aware Background Retrieval
We propose a two-stage retrieval strategy that combines CLIP's high-level semantic features with ResNet's levelevel style descriptors search existing image database. The method retrieves images whose semantics and appearance are most similar to the target domain, providing background candidates that better match the target-domain distribution and thus enrich the support set S.
In practice, given a support image I, we remove the ground-truth bounding box with LaMa in- painting [49] to obtain a background without foreground bint- We use the CLIP encoder extract embeddings from the initial background binit and the database Doases which we refer to as Fig and Frase. respectively. We compute cosine similarity between the visual feature of the current background query Fbg and the CLIP embedding of each sample in the database. The top m most similar images selected based on this similarity ranking, forming the candidate set Bedippm> which contains m images of the form batip- The subscript notation indicates that the set is constructed using CLIP vision encoder and contains m elements.
Building on this step, we re-rank the hatip by extracting low-level style descriptors using shallow-layer ResNet features. For each background image batip retrieved by CLIP, we extract its low-level feature

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map F using the early layers of a ResNet encoder. We further compute the per-channel mean fe and standard deviation oe by averaging over the spatial dimensions of the feature map F. Concatenating the means and standard deviations over all channels yields a 128-D style vector as,
s(belip) = [ PC, o1,..., ,cl ] E I2C..
For each retrieval candidate betip in the set Beelip.m:. compute the style distance the L2 norm between the style features of the original image basst and the candidate:
d [|s(biitt - s (bccip)ll2-
Here, each corresponds a candidate in Beolip, m>. and we use the distance to rank and select the most stylistically similar backgrounds. We then re-rank the CLIP-retrieved candidates based on their style distances and retain the top n images that are most similar in style. The resulting set of selected images is denoted as Ber,, n>- The images indexed by B Bere, as serve as style-matched references for the subsequent background generation stage.
3.2 Guided Background Generation
To fully leverage the retrieved images while keeping the generation process training-free, we adopt the Flux-Redux model 23 to encode each image into prompt embedding. Given our domain- aware retrieval results Bere, n>, let redux(-) denote FLUX-Redde encoder and FLUX(-) denote the FLUX generator. For each support image, we extract its clean background embedding Flg = redux(benet and the embedding of the top retrieved image bye Bere, n> as Fre = redux (bra). We then fuse them as Fdom A Pbg Frer where X and A2 are hyper parameters.
Finally, the FLUX generator produces diverse background images at 1024 x 1024 resolution by applying generative function FLUX to the domain embedding Fdoo i.e., bdom F-UXXFFdomm Fdom). We sample this process n times to generate of diverse images (b(2), ((2), ,..., b(1]).
3.3
Brrgrround-Backroouue Composition
Based on the diverse backgrounds generated in the previous stage, we aim to seamlessly integrate new backgrounds into the original images while preserving foreground pixels and maintaining the target-domain distribution. To achieve this, we employ Flux-Fill for outpainting. Specifically, for each corresponding support image I, construct binary mask M E (0, RxW, The mask is computed based on the ground-truth bounding box bbox(x) as:
[0, if E bbox(x), for each E Ωzz
M(p)
1, otherwise.
where Pg denotes the spatial domain of image x. and indexes a pixel location. We then extract the prompt embedding F gen by Fgen redux(baom) and feed (ær, M. Fgen) into Flux-Fill. To preserve foreground details, Flux-Fill encodes the input x using a VAE and blends the encoded latent features with the initial noise. However, due the VAE-based downsampling, struggles to retain fine-grained structures such as small objects. To mitigate this issue, before generation, we denote the up-sampling method Sup on each image as,
0, if width(a:) > 1024 and height(x) > 1024,
Sup
1,
otherwise.
After generation, we denote a corresponding down-sampling method Sdown as,
0, if Sup (u) = O,
Sdown (x)
(5)
1, otherwise.
The model then repaints only the masked regions, merging the style of baom while keeping the foreground object's appearance and position unchanged. The final output of Domain-RAG, denoted x is given by.
+ = ddown (Flux-Fill (x), (M)) Fgen)).
(6)
This completes the foreground bwwoouuon--bkkgouudnd composition, yielding an augmented support image with domain-aligner background and unchanged foreground objects.
5

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3.4 Applying Domain-RAG to CD-FSOD
In principle, our proposed Domain-RAse RAA framework can be seamlessly integrated with any existing detector to enhance performance in cross-domain scenarios. As training-free plug-and-play data augmentation module, Domain-RAGE requires no modification to the detection architecture or training pipeline. Once the augmented support images are generated, the model is fine-tuned on the combination of the original support set 8 and the generated samples. At inference time, Domain-RAd -RAG not involved; the detector is evaluated directly on original query set Q
4 Experiments
Setups. We conduct experiments on three FSOD tasks with domain shifts: 1) CD-FSOD: Following
the CD-ViTO benchmark [10], we evaluate six diverse target domains: ArTaxOr [6] (photore-
alistic), Clipart [17] (cartoon), DIOR (26) (acrial), DeepFish [46] (underwater), NEU-DET [47]
(industrial), and UODD [18] (underwater). 2) Remote Sensing FSOD (RS-FSOD): In addition to DIOR, we include NWPU VHR-10 41), a popular remote sensing dataset for FSOD. 3) Camou- flaged FSOD: We also test CAMO-FS [99), recent dataset with 47 categories where objects deliberately camouflaged into the background. For each task, we follow the standard dataset splits and evaluation protocols: 1/5/10-shot for CD-FSOD, 3/5/10/20-shot for RS-FSOD, and 1/2/3/5-shot for Camouflaged FSOD. Results are reported using mean Average Precision (mAP).
Table Main results (mAP) on the CD-FSOD benchmark under the 1/5/10-shot setting. f marks methods implemented reproduced by Best results are highlighted in pink.
Method
Backbone
ArTaxOr
Clipartik
DIOR
DeepFish
NEU-DET
UODD
Average
Meta-RCNN
53
ResNet50
2.8
7.8
3.6
TFA w/cos
51
ResNet50
3.1
8.0
4.4
FSCE 48
ResNet50
3.7
8.6
3.9
DeFRCN
ResNe150
3.6
Distill-edfsed
59
ResNet50
5.1
7.6
10.5
NaN
NaN
5.9
ViTDeT-FT
28
VIT-B/14
5.9
6.1
12.9
0.9
2.4
4.0
5.4
Detic 62
ViT-114
Detic-FT 67
VIT-L114
3.2
15.1
4.1
9.0
3.8
4.2
6.6
DE 58
VIT-/114
0.4
0.5
2.7
0.4
0.4
1.5
1.0
CD-VITO
VIT-L/14
21.0
17.7
20.3
3.1
13.9
Groundigg
Swin-B
26.3
55.3
14.8
36.4
13.9
26.3
ETS/ 42
Swin-B
28.1
55.8
12.7
39.3
11.7
18.9
27.8
Domain- RAG (Ours)
Swin-B
57.2
56.1
38.0
12.1
20.2
33.6
Meta-RCNN
53
ResNe150
8.5
17.7
8.8
TFA w/cos
ReaNe150
8.8
18.1
FSCE 48
ResNet50
10.2
18.7
9.6
DeFRCN
45
ResNe150
9.9
18.9
9.9
Distill-edfsed
52
ReaNe150
12.5
19.1
16.0
12.2
16.4
VITIXT-FTT
28
WIT-B/14
20.9
23.3
23.3
9.0
13.5
11.1
16.9
Detic 62
VIT-L/14
0.6
11.4
0.1
0.9
0.0
0.0
2.2
Detic-FT
VIT-L/14
8.7
20.2
12.1
14.1
10.4
DE-VIT 58
WT-1114
10.1
5.5
7.8
2,5
1.5
3.1
CD-VITO 10
VIT-/144
47.9
41.1
26.9
22.3
11.4
6.8
26.1
Grounding
A3
Swin-B
68.4
57.6
29.6
41.6
19.7
25.6
40.4
ETSt 42
Swin-B
64.5
59.7
29.3
42.1
23.5
27.7
41.1
Homainn-RAG. (Ours)
Swin-B
70,0
$9.8
31.5
43.8
24.2
26.8
42.7
Meta-RCNN
33
ResNet50
14.0
20.6
11.2
TFA w/ens
ResNe150
14.8
20.5
11.8
FSCE
48
ReaNe150
15.9
21.9
12.0
DeFRCNN
441
ResNet50
15.5
22.9
12.1
Distill-cdfsod
52
ResNe150
18.1
27.3
26.5
15.5
21.1
14.5
20.5
10-hhot
VTT
28
23.4
Detic
62
VIT-L114
0.6
11.4
0.1
0.9
0.0
0.0
2.2
Detic-FT
VI--L/14
12.0
22.3
15.4
17.9
16.8
14.4
16.5
9.2
11.0
2.1
1.8
3.1
VT-O
VIT-/114
60.5
44.3
30.8
22.3
12.8
7.0
29.6
GroundiingDINO0
Swin-B
73.0
58.6
37.2
38.5
25.5
30.3
43.9
ETSt 42
Swin-B
70.6
60.8
37.5
28.3
44.4
Domain-RAt (Ours)
Swin-B
73.4
61.1
39.0
41.3
26.3
31.2
45.4
132

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Implementation Details. We use pretrained GroundingDINO [3] with Swin- Transformer [3 Base (Swin-Bi B) as backbone as our baseline. For the retrieval stage, the hyper parameters are set to m 100, = 5, throughout all experiments. For the background generation stage, fusion hyper parameters X and X2 are set to 1.0 and 0.8 respectively. We fine-tune the model for 30 epochs by default, but reduce to 5 for faster-converging datasets like Clipart1k and DeepFish. We use AdamW [36] with learning rate and weight decay set to 1 x 10-4, and we scale the backbone's learning rate by 0.1. All experiments are run on four Tesla V100 GPUs eight 5880 Ada GPUs, or single A800 GPU. Further details are the Appendix.
4.1 Main Comparison Results
CD-FSOD Results. Tab. summmaizess the main comparison results on CD-FSOD under 1/5/10
shots across six novel targets. Particularly, we include several competitors: Meta-RCNN [53], TFA
w/cos [51]. FSCE [48) DcFRCN [44). Distill-cdfsod [52]. ViTDeT-FT [28], Detic/Detic-FT [62].
DE-ViT (58) as reported in VDTO [T]. In addition, we also report the results of fine-tuned
GroundingDINO [33, ETS 42 to compare with Domain-RAG. Note that both ETS and Domain-
RAG build on GIo but with different augmentation strategies.
We highlight that Domain-RAs consistently outperforms existing competitors across most target domains, achieving new state-of-the-art (SOTA) results. Compared to the GroundingDINO baseline, our method improves mAP by 7.3, 2.3, and 1.5 points under the 1, 5, and 10 shots, respectively. These results not only show the effectiveness of Domain-RAG, but also reveal its superiority over other proposed augmentation strategies such ETS. Beyond the average gains, also notice: 1) Significant gains on ArTaxOr: Domain-RACC achieves 117.5% relative improvement in the 1-shot setting. We attribute this to the strong semantic and visual compatibility between Ar Arx and the retrieved COCO-style backgrounds, where ArTaxOr features the fine-grain fineggriined foreground but with a relatively close visual domain to COCO regarding background. 2) Robusteess under low-shot settings. The largest gains are observed in the 1-shot scenario, which is the most challenging FSOD scenario. This shows our benefits under severe data scarcity. 3) Strong generalization severe domain shift. On NEU-DET, an industrial defect detection dataset characterized by uncommon objects and background styles, Domain-RAC consistently improves all shot settings, demonstrating its capability handle the most challenging cross-domain FSOD cases.
RS-FSOD Results. Tab. summmrizes the results the NWPU VHR-10 10 remote sensing dataset [4]] under the 3/5/10/20-shot settings. The dataset is divided into base classes and novel classes. The table is split into parts. In the upper part, we follow the standard RS-FSOD protocol: models are first trained on the base classes and then fine-tuned and evaluated on the novel classes. Under this setting, the base classes contain sufficient number of annotated samples, and we apply augmentation strategy on top of the previous state-of-the-art method SEA-FSDet (34), and report the mean Average Precision (mAP) a CD-FSOD setting, where the pretrained model is directly fine-tuned on all 10 classes (both base and novel), each with only few labeled samples. To ensure comparability with the upper setting, the reported mAP here reflects performance exclusively on the three novel categories.
the novel classes. In the lower part, explore the dataset in
Table 2: Main results (mAP) on the NWPU VHR-10 benchmark under the 3/5/10/20-shar settings. The upper part follows the standard RS-FSOD problem setup, while the lower part adapts CD-FSOD setup, with the best results highlighted in pink. means results are produced by us.
Method
Training Setting
Backhone
3-shot
5-shot
10-shot
20-shot
Average
Meta-
53
RS-FSOD
ResNet-50
20.51
21.77
26.98
28.24
24.38
FsDetView
19
RS-FSOD
ResNet-50
24.56
29.55
31.77
32.73
29.65
TEA w/cos
51
RS-FSOD
ResNet-50
16.17
20.49
21.22
21.57
19.86
P-CNN
RS-PSOD
ResNet-50
41.80
49.17
63.29
66.83
55.27
ESOD
RS-FSOD
ResNet-50
10.95
15.13
16.23
17.11
14.86
FSCE
RS-FSOD
55
41.63
48.80
59.97
79.60
57.50
ICPE
37
RS-FSOD
ResNet-50
6.10
9.10
12.00
12.20
9.85
VFA
RS-FSOD
ResNet-50
13.14
15.08
13.89
20.18
15.57
SAE-FSDet
34
RS- RS-DDOD
ResNet-50
57.96
59.40
71.02
85.08
68.36
Domain-RAn (Ours)
RS-FSOD
Re
59.99
65.78
72.87
84.05
70.67
GroundingDINO
33
CD-FSOD
Swin-B
$7.1
61.3
65.1
69.5
63.3
Domain-Ran -RA (Ours)
CD-FSOD
Swin-B
58.2
62.1
66.6
69.7
64.2
7

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Notably, from the upper standard RS-FSOD results, we observe the following findings: 1) Our Domain-RAs achieves the best result via improving the strong SEA-FSDet, achieving 2.31 mAP improvement across all shots average. This indicates that our plug-and-play augmented method is compatible with existing methods. Minor decrease is observed for 20-shot, from 85.08 to 84.05. We speculate that it is due to the base training being sufficient. Further augmentation in this regime may lead to overfitting on synthetic data patterns rather than benefiting novel-class generalization. From the lower part of the CD-FSOD setting results, we highlight that our method again improves the strong GroundingDINO baseline, indicating its effectiveness.
Camouflaged FSOD Results. Tab. presents the results on the CAMO-FS [39 under 1/2/3/5 shots. All categories in this dataset are treated as novel classes and are further split into support set and query set, naturally aligning with the formulation of CD-FSOD. The first two rows in the table report the results of CDISS-ITI." and "FS-CDIS-IMS" two methods developed from the original CAMO- FS paper. Below that, we include our reproduced baseline using Grounding as the detector, along with the results of our proposed Domain-RAn method built on top of GroundingDINO.
As shown by the results, the large-scale pretrained model, i.e., GIONndingDINO, brings a substantial performance boost this task, improving results from around 7 over 65 mAP.
We believe this remarkable advancement will advance the frontier this field. Moreover, the performance gains introduced by proposed method over the DrO baseline remain consistently clear across all shot settings. The consistent success across CD-FSOD, RS-FSOD, and camouflaged FSOD, covering eight challenging and diverse domains, demonstrates that method serves as general and effective solution for addressing the gap issue few-shot object detection.
Table 3: Main results (mAP) on the Camouflage FSOD under the 1/2/3/5-shot settings. means the results are produced by us, the best results are highlighted in pink.
Method
Backbone
1-shot
2-shot
3-shot
5-shot
Average
FS-CDIS-ITL 39
ResNet-101
4.0
7.3
7.5
9.8
7.1
FS-CDIS-IMG 39
ResNet-101
4.5
7.0
7.6
10.4
7.4
GroundingDINOO
33
Swin-B
63.4
66.8
67.1
69.1
66.6
Domain-RAG (Ours)
Swin-B
65.5
67.7
68.3
70.3
68.0
4.2 Comparison with Other Augmentation Methods
To assess the effectiveness of Domain-RAC framework, compare it with several strong baselines that are designed for augmenting data for CD-FSOD. Specifically, 1) "Copy-Paste" directly overlays foreground objects onto random COCO backgrounds without considering semantic relevance or compositional integrity. 2) "Foreground Augmentation" attempts to diversify object appearances by inpainting new foregrounds after object removal. This is done by using the category label of each bounding box a text prompt and applying SDXL-inpaint to generate new foreground after removing the original object. 3) "Background Augmentation", which we use InstructB (5) to caption the remaining background, and guide SDXL to generate a new background based on this caption after removing the foreground from target image. To ensure fair comparison, all the augmentation methods use G 5. Comparison results are summarized in Tab. 4 The results are reported on CD-FSOD under the 1-shot setting.
Table 4: Comparison of augmentation methods (mAP) on the CD-FSOD benchmark under 1-shot.
Method
ArTaxOr
Clipart
DIOR
DeepFish
NEU-DET
UODD
Average
GroundingDINO
26.3
55.3
14,8
36.4
9.3
15.9
26.3
Paste
38.8
55.0
15.0
36.4
8.4
14.2
27.9
Foreground Augmentation
32.4
56.1
13.9
41.4
9.6
14.9
28.1
Background Augmentation
52.3
S3.7
16.9
34.2
8.9
10.8
29.5
Domain-Ran RAG (Ours)
57.2
56.1
18.0
38.0
12.1
20.2
33.6

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We observe that: 1) copy-pasee paste methods can work reasonably well on relatively simple datasets such as ArTaxOr. However, due lack of semantic consistency and domain alignment, they tend to fail on most target domains. 2) prreground-auggentaiie visually simple and isolated, for example, datasets like DeepFish, where only a single object is present. However, due to the potential semantic shift issue, it failed more complex datasets such as DIOR and UODD. 3) birkground-augmenttane failing datasets with distinctive domain characteristics, such NEU-DET. 4) In contrast, our method consistently improves upon the baseline across all datasets, demonstrating its robustness and effectively addressing the limitations of prior approaches.
baseline performs well when the foreground
baseline also suffers in CD-FSOD, often
4.3 More Analysis
Ablation Study on Proposed Modules. To evaluate each module's effectiveness, we conduct
ablation studies by removing or replacing with naive alternatives. As a typical challenging case. the NEU-DET under 1-shot setting is demonstrated as an example. Results are shown in Fig. 5
(a). Specifically, 1) the grey bar marks the "baseline", i.e. vanilla fine-tuned GroundingDINO. 2)
The pink bar ("w/o background retrieval") disables the domain-aware retrieval module and replaces the backgrounds with random COCO images while keeping the rest the pipeline unchanged. 3) The yellow bar ("w/o background generation")) skips the nomain-guided background generation and directly performs the foreground- foreground-backgrouus backgrounddd composition with the raw retrieved images from COCO. 4) The blue bar ("copy-iy ppppte as compositional") removes the last composition stage and simply pastes the foreground onto the domain-alignes generated background. 5) The last colorful bar represents our full Domain-RAG.
Results show that our full model outperforms all ablated variants, achieving the best overall perfor- mance. Furthermore, we observe the following: 1) By comparing our method with the pink bar, we verify that the domain-aware background retrieval stage provides backgrounds that are better aligned with the target domain. 2) The comparison between the gray and yellow bars indicates that simply augmenting backgrounds using COCO images offers limited benefits. In contrast, the domain- guided background generation stage significantly improves performance by producing backgrounds that are both semantically and stylistically aligned, as evidenced by the gap between the yellow and final colorful bars. 3) The performance drop seen with the blue bar underscores the importance of the foreground- background composition stage, which enables seamless integration foreground objects into the generated backgrounds. Together, these observations confirm that each component of Domain-RAl is both indispensable and complementary for achieving robust CD-FSOD performance.
The Construction of RAG Database In the defined (closed-source) CD-FSOD setting proposed in CD-ViTO 10], COCO serves as the only single-source dataset for training, while other datasets (ArTaxOr, Clipartlk, DIOR, DeepFish, NEU-DET, UODD) are treated as unseen targets. Using COCO as the RAG database brings two key advantages: (1) does not introduce any extra data beyond the default setting, ensuring the fairness of comparison; (2) COCO provides diverse and general-domain backgrounds that better cover novel domain scenarios.
Furthermore, we conducted additional experiments using different database options, including COCO with reduced category numbers, NEU-DET geweral-deeaaiiiii and minilmageNet.
Table 5: Effect of different database choices on Domain-RAN performance.
DataBase
ArTaxOr Clipart1k
DIOR
FISH
NEU-DET UODD
Avg
Base (GroundingDINO)
26.3
55.3
14.8
36.4
9.3
15.9
26.3
COCO-Iclass
50.1
55.0
15.7
36.6
12.0
16.0
30.9
COCO-5classes
51.0
55.1
16.6
36.7
11.9
17.5
31.5
COCO-20clane
53.0
56.2
16.2
37.0
12.2
18.9
32.3
COCO-80classe (Ours)
57.2
56.1
18.0
38.0
12.1
20.2
33.6
NEU-DET
49.8
55.2
16.4
37.0
12.0
16.1
31.1
minilmageNet
55.6
53.2
15.6
38.0
14.0
16.2
32.1
From the results summarized Table we observe that: (1) broader category coverage consistently improves performance; (2) general-domain databases such COCO outperform specific-domain

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Ablation Study Medules
Visualization Image Gencrated Image
Failure Cases
Figure 3: (a) Ablation study on modules, results reported on NEU-DET, 1 shot. (b) Visualization of target image (top row) and generated image (second row). (c) Failure Cases.
ones like NEU-DET; and (3) although minilmageNet can serve as an alternative database, performs slightly worse than COCO due to its larger foreground regions and less diverse backgrounds. These findings demonstrate that our Domain-RAl consistently enhances the base GroundingDINO across all benchmarks, validating the robustness and effectiveness of our approach.
Visualization of Generation Images. To provide a more intuitive illustration of our method's effectiveness, we present qualitative results in Fig. (b). Each example shows the original target image from a different domain in the first row and the corresponding generated image in the second row, with annotated object bounding boxes. From the results, we observe the following: 1) Our method effectively preserves the foreground object without introducing noticeable changes, even in challenging domains such as remote sensing, underwater, and industrial defect scenarios. 2) The generated images successfully introduce new backgrounds while maintaining overall semantic coherence and visual consistency with the original domain. Also, the outputs appear natural and realistic. These two observations align well with our goals and further validate the effectiveness of the proposed Domain-RACC framework.
Failure Cases and Limitations. We further examine the quality of the generated images and observe that, in few cases, our model cxhibits foreground information leakage. Parts of the foreground object are unintentionally regenerated within the background, as illustrated in the area highlighted with red boxes of Fig. (c). Since these regenerated foregrounds are not explicitly controlled and lack corresponding annotations, they may introduce noise into model fine-tuning and potentially harm detection performance.
Additional results, including ablation studies of our proposed modules other targets, more detailed analyses, and extended visualizations, are provided in the Appendix.
5 Conclusion
In this paper, we investigate few-shot object detection (FSOD) across domains-a more realistic yet significantly more challenging scenario than conventional FSOD. We focus on three representative tasks: cross-domain FSOD (CD-FSOD), remote sensing FSOD (RS-FSOD), and camouflaged FSOD. To improve performance under these settings, we propose Domain-RAG, training-free compositional image generation framework designed to produce Nomain-aligned and detection- friendly samples. Unlike existing text-to-image generation approaches that rely solely on textual prompts, Domain-RAG retrieves semantically and stylistically similar images as structured priors to guide the generation process. To the best of our knowledge, this is the first application of retrieval- augmented generation to cross- cross-domain object detection, particularly in a training -free way suitable state-of- -f-thh--rttttttt results across all three tasks,
for low-shot scenarios. Domain-RA. RAG achieves demonstrating its generalization ability and opening new directions for training-free data synthesis.
Acknowledgment
This work was supported by the Science and Technology Commission Shanghai Municipality (No. 2451 1 1 1001100).TTT The authors gratefully thank the organization for their support and resources.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state that the paper proposes DomainRAG, a training-free, plug-and-play background augmentation framework designed for cross- domain few-shot object detection. We explicitly frame the task as CD-FSOD, describe the retrieval- eetrevallaugmmented generation pipeline, and clarify that improvements are demonstrated across multiple datasets.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The paper discusses a specific limitation of the proposed image generation method, namely the unintended foreground information leakage into the background. This phenomenon, highlighted in Fig. (ic), identified as source of noise that may negatively impact model fine-tund -eenitunngg and downstream detection performance. The authors acknowledge this issue and explain its implications, demonstrating awareness of their method's 's boundaries.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best
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Reviews:
[{"review_id": "xPKxcE4Y9E", "reviewer": "Reviewer_1qWt", "summary": "Few-shot object detection using generated image conditioned on RAG DB is a novel and interesting approach from this paper. It resolved the problem about domain different regarding few-shot example from source domain to the target test domain. The proposed Domain-RAG framework is training-free and operates on the principle of \"fix the foreground, adapt the background\". Benchmark results show a superior performance across multiple datasets particularly in the most few-shot especially 1-shot) settings.", "questions": "1. how the foreground object selected?\n\n2. how the cross-domain test setup can make sure the performance once the target test sample is different domain from the RAG DB?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.\tThe proposed method is designed well by combing the DB for retrieval augmentation generation manner to find the similar background but masking the foreground and use the searched background as condition for generating new background with similar domain pasting the foreground object as a seamless and natural generation. The proposal is easily to be adapted to the existed cross-domain object detection method. \n\n2.\tThe proposed method performed well on the few-shot cross-domain object detection benchmark especially the most challenge 1-shot setup. with +7.3 mAP in the CD-FSOD setting. \n\n3.\tThe paper is organized well and and easy to follow. The methodology is explained clearly, the evaluation setup is designed well and to show the main result clearly supporting the proposed approach.\n\nWeaknesses:\n\n1.\tThe method relied on the external knowledge from RAG DB, where the quality of the DB and how to create the DB is less of discussion toward how it affected the performance. And how to balance the fairness compared to the other method which does not use the external knowledge is better to discuss. \n\n2.\tThere is lack of ablation discussion about inpainting with and without RAG DB as condition. For the qualitative and quantity result, it is less of the discussion about the retrieval performance and retrieved image how it looks like, and also less of ablation on the generated sample number $n$.\n\n3. The proposed is a combination of the existed model for the critical module used in the pipeline such as the inpainting model, Flux-Redux model, detection model that less of optimization and novelty on the model side but focus on the entire pipeline, however, the pipeline itself is interesting and still have space to optimized as insight for the community.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for the positive and constructive feedback! We really appreciate that you consider our work “well-designed”, “seamless and natural generation”, “easy-to-adapt”, “performed well”, and that our writing “well organized and easy to follow”. Below, we address the concerns raised.\n\n# W1-1: More Discussion on the Quality and Construction of RAG DB.\n\nThanks. We first clarify the reasons for choosing COCO as DB and then discuss more on how the quantity of DB affects the results.\n\n-   **Reasons for taking COCO as DB.** In the defined (closed-source) CD-FSOD setting as proposed in CD-ViTO[10], COCO serves as the only single source data for training, while the others (ArTaxOr, Clipart1k, DIOR, DeepFish, NEU-DET, UODD) work as unseen targets. Using COCO as the RAG DB: 1) does not introduce extra data beyond the default setting, protecting the fairness of comparison. 2) As in L49, we hope to generate diverse backgrounds for the query image, which COCO fits well with diverse categories and general-domain backgrounds, which can better cover novel domains.\n-   **How Quantity of DB Affects Results.** Upon question, we further conduct more experiments with different DB options, including COCO as DB but with reduced category numbers, NEU-DET (non-general domain) as DB, and also miniImageNet as DB (originally reported in Tab.6, Appendix)\n\nFrom the results below, we show that: 1) broader category coverage improves performance. 2) More general-domain DB (COCO) works better than\nspecific-domain (NEU-DET). 3) As discussed in L927-L935, mini-ImageNet also works as DB, will be inferior to COCO preliminary due to its larger foreground, i.e., smaller background. We highlight that our Domain-RAG improves the base GroundingDINO in all the cases, and solidly validates our effectiveness.\n\n| Method              | DB                   | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg   |\n|:--------------------|:---------------------|--------:|----------:|-----:|-----:|--------:|-----:|------:|\n| Base GroundingDINO   | -                    |    26.3 |      55.3 | 14.8 | 36.4 |     9.3 | 15.9 |  26.3 |\n| Domain-RAG          | COCO-1classes        |    50.1 |      55.0 | 15.7 | 36.6 |    12.0 | 16.0 |  30.9 |\n| Domain-RAG          | COCO-5classes        |    51.0 |      55.1 | 16.6 | 36.7 |    11.9 | 17.5 |  31.5 |\n| Domain-RAG          | COCO-20classes       |    53.0 |      56.2 | 16.2 | 37.0 |    12.2 | 18.9 |  32.3 |\n| Domain-RAG          | **COCO-80classes (Ours)**|    **57.2** |      **56.1** | **18.0** | **38.0** |    **12.1** | **20.2** |  **33.6** |\n| Domain-RAG          | NEU-DET              |    49.8 |      55.2 | 16.4 | 37.0 |    12.0 | 16.1 |  31.1 |\n| Domain-RAG          | miniImageNet (Tab.6) |    55.6 |      53.2 | 15.6 | 38.0 |    14.0 | 16.2 |  32.1 |\n\n\n# W1-2: Fairness of Using RAG DB.\nThanks, we will clarify that: \n\n1\\) As we mentioned in the reason for taking COCO as DB, it is defined as the source in the default CD-FSOD setting; thus, using COCO should be regarded as a different way of leveraging the same source data. \n2) Several compared methods include ETS (27.8, Tab.1),  Copy-Paste (27.9, Tab.4), Foreground Aug. (28.1, Tab.4), and Background Aug. (29.5, Tab.4), don’t use COCO in a RAG-inspired way, but share the exact same baseline and training configuration with us, and also fall into the data augmentation-based approaches. The performance gap between them and our Domain-RAG (33.6, Tab.1) on 1-shot instead validates the superiority of our RAG-based method, which has not been proposed in this task before. 3) Additionally, the ablation experiments on Fig.3(a) and Tab.5 (Appendix) all use COCO as DB without applying the complete Domain-RAG method. We believe these results confirm that the improvements stem from our pipeline design rather than merely the use of COCO as an external database.\n\n We will include the new results and discussions in the revised paper.\n\n# W2-1: Ablation on Inpainting with/without RAG DB as Condition.\nThanks. In Fig. 3(a) and Tab.5 (Appendix), we reported results for w/o Background Retrieval (random COCO background), which shows only limited improvement (31.8 vs Ours: 33.6, 1-shot). To further address your concern, we added experiments for w/o RAG DB (no DB conditioning, where the original image background is used during Domain-Guided Background Generation). We highlight: 1) the comparison results between “w/o RAG DB” vs. “w/o Background Retrieval” or our Domain-RAG indicate that the DB conditioning contributes significantly to final performance,  bringing background diversity into the extremely limited target images. 2) The comparison results between  “w/o Background Retrieval” vs. our Domain-RAG demonstrate that our domain-aware background retrieval stage provides backgrounds that are better aligned with the target domain (as in L308-309, L922). \n\n| Method                          | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg. |\n| :------------------------------ | ------: | --------: | ---: | ---: | ------: | ---: | ---: |\n| Baseline (GroundingDino)        |    26.3 |      55.3 | 14.8 | 36.4 |     9.3 | 15.9 | 26.3 |\n| w/o RAG DB                      |    34.0 |      56.1 | 16.2 | 37.2 |     8.9 | 14.9 | 27.9 |\n| w/o Background Retrieval (Tab.5)|    51.7 |      57.3 | 17.0 | 37.2 |    10.9 | 16.4 | 31.8 |\n| Domain-RAG (Ours)               |    57.2 |      56.1 | 18.0 | 38.0 |    12.1 | 20.2 | 33.6 |\n\n# W2-2: Discussion on Retrieval Performance and Retrieved Image.\nWe appreciate this point and thus evaluate the relevance between retrieved samples and its query through human judgment under the 1-shot setting. For most datasets, over 80% of retrieved samples were deemed relevant, except for NEU-DET, where the relevance was lower due to the lack of similar backgrounds in COCO. We also attempted to use GPT-4o for relevance evaluation, but it struggled with context-specific reasoning (e.g., for insect datasets, GPT-4o considered leaves and grass as different contexts, despite both being suitable as natural backgrounds).\n\n| Metric       | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD   |\n|:-------------|--------:|----------:|-----:|-----:|--------:|-------:|\n| Irrelevant   |       2 |         5 |   17 |    0 |      14 |      2 |\n| Relevant     |      33 |        95 |   83 |    5 |      21 |     13 |\n| Total-gen    |      35 |       100 |  100 |    5 |      35 |     15 |\n| Percentage| 94.2%|   95% | 83%|100%|  60%| 86.6%|\n\nFurthermore, we 1) provided the examples of the retrieved background in Fig.5 (Appendix, 2nd column), it shows we managed to retrieval domain-consistent, semantic aligned backgrounds, and 2) again the results summarized in W2-1 between “w/o Background Retrieval (random COCO background) (31.8)” and ours (33.6) well validate that our retrieval images perform well for subsequent image generation.\n\n# W2-3:  Less of Ablation on the Generated Sample Number n.\nThanks, we actually provided an ablation study on the number of generated samples in Tab.8, Appendix (where “m” was incorrectly written instead of “n”; this will be corrected). Results show that performance improves significantly as n increases from 1 to 3, achieves its best beyond n=5, and drops when n is set to 10. As in L941-950, we analyze that too few retrieved images limit visual variation, while too many increase the chance of unrealistic compositions. \n\n### W3: Potential Optimization of the Pipeline. \nThanks for recognizing our pipeline as interesting and proposing this suggestion.  While our method builds upon prior diffusion-based inpainting and detection backbones, its key novelty lies in the training-free principle of “fix the foreground, adapt the background” and the carefully integrated pipeline design for addressing the cross-domain image generation. We also considered optimization, for example, applying efficient finetuning, e.g, LoRA, to better fit into targets. However, given extremely few-label examples, e.g,1 shot and also with the domain shift issue, finetuning the model, no matter the number of learnable parameters, doesn’t help improve performance and will burden the current procedure, limiting the quick deployment of our method.  In contact, our model-agnostic, training-free approach provides a practical and broadly applicable solution.\n\n# Q1: Foreground Object Selection.  \nThanks, since we have the object bounding boxes for those support images, i.e., query images to be augmented, we simply fix the objects labeled by those bounding boxes. \n\n# Q2: Performance When the Test Sample is Different from the RAG DB.\nThanks, we appreciate this question, while we would like to highlight that: 1) our used CD-FSOD benchmark already include the challenging cases where the target domain is far away from the RAG-DB (COCO), covering domains such as fine-grained animals, cartoon-style, remote sensing, underwater, and industry.  For instance, DIOR is a remote sensing image, which is different from the COCO in the visual domain, resolution, camera perspective, and objects. UODD is a more difficult case, containing industry images in gray color. Examples can be found in Fig.3.  2) Even taking NEU-DET, a domain with an extremely large gap with all the others, as the RAG DB, the table below shows we still increase performance over baseline GroundingDINO.\n\n| Method                           | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg. |\n| :------------------------------- | ------: | --------: | ---: | ---: | ------: | ---: | ---: |\n| Baseline (GroundingDINO)         |    26.3 |      55.3 | 14.8 | 36.4 |     9.3 | 15.9 | 26.3 |\n| Domain-RAG (NEU-DET as RAG DB)   |    49.8 |      55.2 | 16.4 | 37.0 |    12.0 | 16.1 | 31.1 |\n\nWe hope that our response adequately addresses your concerns. We are happy to provide more information during the discussion phase."}, {"review_id": "r5qXjmgS8J", "reviewer": "Reviewer_wq5x", "summary": "This paper addresses the problem of Cross-Domain Few-Shot Object Detection (CD-FSOD), where the goal is to detect previously unseen objects in a new domain using only a handful of labeled examples. The authors introduce Domain-RAG, a retrieval-guided, training-free image compositional generation framework that generates domain-aligned synthetic training samples by preserving the foreground and synthesizing new, stylistically and semantically matched backgrounds. The paper details the method's three key components: domain-aware background retrieval, domain-guided background generation, and foreground-background composition on multiple cross-domain FSOD tasks, setting new state-of-the-art results across CD-FSOD, remote sensing FSOD, and camouflaged FSOD benchmarks.", "questions": "1: How does database bias or incompleteness in the retrieval gallery affect the robustness and effectiveness of Domain-RAG? Have the authors considered using or simulating more limited/biased galleries or out-of-distribution domains?\n\n2: Have the hyperparameters for retrieval (m, n) and blending coefficients ($\\lambda_1$, $\\lambda_2$) been systematically explored for different domains or settings? Is the method robust to these values, or does it need careful tuning?\n\n3: Could a post-filtering step be introduced to address foreground leakage/failure cases (e.g., a simple trained foreground/background discriminator, or detection model–based filtering)?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1: The proposed Domain-RAG does not require any model retraining or extra supervision, making it easily adaptable and complementary to various detectors without end-to-end pipeline changes.\n\n2: The method is motivated by shortcomings of existing data augmentation and generation methods for CD-FSOD, and builds on retrieval-augmented generation, a principled approach in the context of domain shift.\n\n3: The approach is extensively validated on three difficult tasks (CD-FSOD, RS-FSOD, camouflaged FSOD) and a variety of domains. Table 1 demonstrates consistent improvements over strong baselines in all evaluated settings. Meanwhile, the paper also provides a thorough ablation analysis (see Figure 3) that critically assesses the contribution of each pipeline component, and visual results (Figure 3b) that illustrate successful compositional alignment and realistic image generation. These concrete results substantiate key claims on both performance and qualitative output.\n\n4: The authors explicitly acknowledge method limitations and provide qualitative diagnostic insights, such as the foreground leakage failure mode in Figure 3c.\n\n\nWeaknesses:\n\n1: **Lack of Ablation on Synthetic Sample Quantity n**. While the core contribution of the paper lies in leveraging generative models for training-free data augmentation, the paper does not provide any ablation study on the number of generated images (i.e., the hyperparameter n). Given that the number of synthesized images directly affects the support set size and potentially the detector's performance, it is important to understand how sensitive the method is to this parameter. Without such analysis, it is difficult to assess the scalability or robustness of the proposed augmentation strategy.\n\n2: **Fairness and Generalizability in Experimental Comparisons**. From my understanding, the proposed Domain-RAG method is detector-agnostic, yet all the main results are reported only on top of GroundingDINO. It remains unclear whether the compared methods (e.g., CD-ViTO, ETS, Detic-FT) were also allowed to use additional synthetic data during training, or were constrained to the original support set only. This raises concerns about the fairness of the comparison. Furthermore, no experiments are provided to test the generalizability of Domain-RAG when applied to other object detectors.\n\n3: The success of the retrieval component is contingent on the **diversity of the background image database (COCO in this work)**. The paper does not thoroughly analyze or quantify the effect of database bias or incompleteness on augmentation quality, which is critical for practical deployment in truly novel domains or non-natural image settings. A discussion on this is missing from Section 4.1, and the experiments focus on domains where retrieval from COCO is plausible.\n\n4: **Analysis on Performance Drop at Higher Shot Settings.** In Section 4.1 and Table 2, the method shows a slight drop in mAP at higher shot counts (e.g., 20-shot in RS-FSOD). The paper mentions that this might be caused by overfitting or too much synthetic data, but this is only a guess. There is no detailed analysis or experiment to confirm the reason or show how to fix it.", "was_revised": true, "final_justification": "After considering the rebuttal, most of my concerns have been addressed. I decide to raise my rating to borderline accept.", "rebuttal": "Thank you very much for the positive and constructive feedback! We really appreciate that you consider our work “easily adaptable”, “motivated by shortcomings of existing methods”, “extensively validated”, and with “ realistic image generation” . Below we address the concerns raised.\n\n# W1: Ablation on Synthetic Sample Quantity n.\nThanks for pointing this out. We actually provided an ablation study on the number of generated samples in Tab.8, Appendix (where “m” was incorrectly written instead of “n”; this will be corrected). Results show that performance improves significantly as n increases from 1 to 3, achieves best beyond n=5, and drops when n is set as 10. As in L941-950, we analysis that too few retrieved images limit visual variation, while too many increase the chance of unrealistic compositions.  \n\n# W2:  Fairness and Generalizability in Experimental Comparisons.\nThanks, we first clarify the reason for building our model on GroundingDINO, discuss more on the fairness, and then provide more results regarding the generalizability.\n\n-   **Reason for building upon GroundingDINO.** The primary reason we report results on GroundingDINO is due to its superior performance compared to other detectors (GroundingDINO, 26.3, 1shot, Tab.1) vs. (CD-ViTO, 13.9, 1shot, Tab.1)  of average mAP,  which we believe will advance this area rapidly. \n-   **Fairness of Comparison Regarding the Usage of Synthetic Data.** First of all, though we use COCO as database to augment new images, COCO itself is defined as the source in the default CD-FSOD setting, we don’t break this formulation. 2) The comparison between ours (33.6, Tab.1) and other competitors e.g., ETS (27.8, Tab.1),  Copy-Paste (27.9, Tab.4), Foreground Aug. (28.1, Tab.4), and Background Aug. (29.5, Tab.4) are fair: we share the exact same baseline and training configuration, and all of them are data augmentation based approaches. For example, ETS proposes a mixture of 6 various augmentation strategies during training; The comparison between ours and CD-ViTO and Detic-FT is unfair, but we clearly notice that we use different baselines in Tab.1, and we are not using these performance gap to show our effectiveness.  3) Additionally, the ablation experiments on Fig.3(a) and Tab.5 (Appendix) all use COCO as DB while without applying the complete Domain-RAG method. We believe these results confirms that the improvements stem from our pipeline design rather than merely the introduction of synthetic data. \n-   **Generalizability of Domain-RAG to Other Detectors.** In addition to building on top of GroundingDINO, we also demonstrated our results on SAE-FSDet, the prior SOTA method on remote sensing FSOD, as in Tab.2.  Upon question, we further tested Domain-RAG on other detectors on DeViT-FT and CD-ViTO.  Below results demonstrate that Domain-RAG is detector-agnostic and can be seamlessly integrated into different architectures without requiring retraining or architectural modifications.\n\n| Method                     | 3-shot | 5-shot | 10-shot | 20-shot                          | Avg   |\n| :------------------------- | -----: | -----: | ------: | :------------------------------- | ----: |\n| SAE-FSDet (Tab.2)          |  57.96 |  59.40 |   71.02 | 85.08                            | 68.36 |\n| SAE-FSDet + Domain-RAG (Tab.2)|  59.99 |  65.78 |   72.87 | 84.05      | 70.67 |\n\n| Method                      | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg  |\n| :-------------------------- | ------: | --------: | ---: | ---: | ------: | ---: | ---: |\n| DeViT-FT                    |    10.5 |      13.0 | 14.7 | 19.3 |     0.6 |  2.4 | 10.1 |\n| DeViT-FT + Domain-RAG       |    11.5 |      17.0 | 15.6 | 19.1 |     2.4 |  3.0 | 11.4 |\n| CD-ViTO                     |    21.0 |      17.7 | 11.7 | 20.3 |     3.6 |  3.1 | 12.9 |\n| CD-ViTO + Domain-RAG        |    22.5 |      20.1 | 15.7 | 22.4 |     3.6 |  3.3 | 14.6 |\n\n# W3 & Q1:  Reliance on the Diversity of the Background Database (COCO). \nThanks, we appreciate this discussion. We would like to clarify that the tested domains are far from being COCO-like. They already involve extremely challenging scenarios with significant category shifts (e.g., novel classes unseen in COCO), severe domain shifts (e.g., clipart-style images, aerial imagery), and also non-natural settings (e.g., industrial textures in NEU-DET). \n\nTo further validate the impact of database diversity and bias, we conducted additional experiments under three conditions: 1) COCO as DB but with reduced category numbers, 2) NEU-DET (non-general domain) as DB, and 3) also miniImageNet as DB (originally reported in Tab.6, Appendix)\n\nFrom the below results, we show that: 1) more broader category coverage improves performance, but even with only 1 COCO category, we still clearly enhance the results.  2) Even when the source domain (e.g., NEU-DET) is far from COCO, Domain-RAG still delivers strong gains through its retrieval-conditioned generation and style adaptation modules, 3) As discussed in L927-L935, mini-ImageNet also works as DB, which is also easy to obtain. \n\nFrom these results, we could conclude that our Domain-RAG improves the base groundingDINO regardless of the database, solidly validate our effectiveness. \n\n| Method              | DB                   | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg   |\n|:--------------------|:---------------------|--------:|----------:|-----:|-----:|--------:|-----:|------:|\n| Base GroundingDINO   | -                    |    26.3 |      55.3 | 14.8 | 36.4 |     9.3 | 15.9 |  26.3 |\n| Domain-RAG          | COCO-1classes        |    50.1 |      55.0 | 15.7 | 36.6 |    12.0 | 16.0 |  30.9 |\n| Domain-RAG          | COCO-5classes        |    51.0 |      55.1 | 16.6 | 36.7 |    11.9 | 17.5 |  31.5 |\n| Domain-RAG          | COCO-20classes       |    53.0 |      56.2 | 16.2 | 37.0 |    12.2 | 18.9 |  32.3 |\n| Domain-RAG          | **COCO-80classes (Ours)**|    **57.2** |      **56.1** | **18.0** | **38.0** |    **12.1** | **20.2** |  **33.6** |\n| Domain-RAG          | NEU-DET              |    49.8 |      55.2 | 16.4 | 37.0 |    12.0 | 16.1 |  31.1 |\n| Domain-RAG          | miniImageNet (Tab.6) |    55.6 |      53.2 | 15.6 | 38.0 |    14.0 | 16.2 |  32.1 |\n\n\n# W3: Analysis on Performance Drop at Higher Shot Settings.\nThanks for pointing this out.  We conducted further analyze to understand the slight mAP drop observed at higher shot counts (e.g., 20-shot in RS-FSOD, Tab.2). Specifically, we varied the number of generated samples (n) under the 20-shot setting in RS-FSOD. Results on VHR-10 are as below.  Results validate that **reducing n alleviates performance degradation** at high shot counts (e.g., 85.48% with n=1 vs. 84.15% with n=3), which better validate our guess in L256 to L257. \nWe highlight that: 1)  This effect is **limited to high-shot scenarios** and can be addressed with a simple adjustment. 2) Domain-RAG only drops in this single case without manually tuning hyperparameter according to the targets,  and achieves **consistent improvements in 1-, 5-, and 10-shot settings** across 3 tasks and 8 distinct target domains. \n\n| Datasets | baseline | NUM=1  | NUM=3  | NUM=5  |\n| :------- | -------: | -----: | -----: | -----: |\n| VHR-10   |    85.08 | **85.48**  | 84.15  | 84.05  |\n\n# Q2: Hyperparameters: Retrieval (m, n), blending coefficients (λ₁, λ₂).\nThanks, 1) the results of different n are answered in W1, which was provided in Tab.8, Appendix;  2) m is set as 100 (L215),  the model is robust to m since as long as it covers n (e.g.,5) images it doesn’t affect the results in principle; 3) the 1shot results of different blending coefficients (λ₁, λ₂) are provided below.  Experiments show that λ₁ = 1 and λ₂ = 0.8 achieve the best balance. When λ₂ = 1, we find λ₁ = 1 performs best. During testing, we slightly reduced λ₂ to 0.8 because setting it too low (e.g., λ₂ = 0.5) distorts semantics and degrades generation quality. This design ensures that domain-specific cues are preserved while still incorporating context from the retrieval database.\n\n| Hyperparameters (λ₁, λ₂) | (0.5,1) | (0.8,1) | (1,1)  | **(1,0.8)** | (1,0.5) |\n|:-------------------------|--------:|--------:|-------:|:-----------:|--------:|\n| **ArTaxOr Performance**  |   51.9  |   52.3  |  53.1  |  **57.2**   |   54.7  |\n\nWe will add these results to the Appendix. \n\n# Q3:  Could Post-Filtering be Introduced to Address Foreground Leakage/Failure Cases?\nThanks for the suggestion. We also considered this ourselves, however, we ultimately decided not to adopt it for the following reasons: \n\n1\\) we analyzed leakage cases and found that among all generated images in the CDFSL,  only very few examples exhibited foreground leakage issue. For example, 1.3% (4/290) under 1shot. \n\n2\\) While post-filtering strategies could be applied, they present significant challenges:\n\n-   Directly applying ZSL inference of existing detectors for foreground detection on CDFSOD is weak, making it difficult for a pseudo-detector to accurately identify leakage. While if we want to apply the trained detector, e.g., trained Domain-RAG (based on GroundingDINO), it will largely burden the pipeline to: data augmentation → train model → filter → train model again. \n-   We also consider applying VLMs (e.g., GPT-4o) to help with filtering. However, they also struggle to meet our expectations. In the meantime, it will also increase the time cost and complexity.\n\nConsidering these trade-offs, and given the extremely low frequency of leakage (1.3%), we decided not to introduce post-filtering as it would not significantly impact overall performance while violating the method’s training-free and lightweight design.\n\n\nWe hope that our response adequately addresses your concerns. We are happy to provide more information during the discussion phase."}, {"review_id": "XXYD3dZ87n", "reviewer": "Reviewer_vndr", "summary": "This paper introduces Domain-RAG, a retrieval-guided compositional image generation framework designed to enhance performance in Cross-Domain Few-Shot Object Detection (CD-FSOD). Domain-RAG follows a training-free three-stage pipeline: (1) domain-aware background retrieval, (2) domain-guided background generation, and (3) foreground-background composition. The method leverages visual retrieval and generative models to synthesize domain-aligned backgrounds that preserve original foreground objects. Extensive experiments demonstrate that Domain-RAG achieves consistent improvements over existing methods, setting new state-of-the-art results in various few-shot detection scenarios.", "questions": "1. In scenarios where the generative models unintentionally leak foreground information into the background (as noted in failure cases), what additional controls or safeguards could mitigate this issue?\n2. How well does this method adapt to zero-shot scenarios?\n3. How are the hyperparameters $\\lambda_1$ccand $\\lambda_2$ selected, and do they significantly affect performance?\n4. The caption of Figure 3 contains a typo. It should be \"(a)\" instead of \"a)\".\n5. How does the number of augmented samples per category affect the performance? How is an appropriate number of synthesized samples determined?", "limitations": "See Questions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Domain-RAG creatively combines retrieval-augmented generation techniques with compositional image synthesis, effectively addressing the critical challenge of domain consistency in CD-FSOD tasks.\n2. The use of retrieval-based visual priors effectively guides the generative process, significantly improving background realism and coherence compared to purely generative methods.\n3. Clear state-of-the-art results are achieved across all evaluated few-shot settings, demonstrating robust performance gains compared to strong baselines.\n\nWeaknesses:\n1. The paper primarily presents empirical findings without rigorous theoretical analysis or deeper conceptual insights, which could limit its novelty for a NeurIPS audience.\n2. The performance significantly depends on the quality and domain relevance of the retrieval database (e.g., COCO), which might limit generalization to datasets drastically different from standard benchmarks.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for the positive and constructive feedback! We really appreciate that you consider our work “creative”, “effective”, “significantly improving background realism and coherence”, and with “clear state-of-the-art results” . Below we address the concerns raised.\n\n# W1: Primarily on Empirical Findings without Rigorous Theoretical Analysis.\nThanks, we agree that our paper focuses on empirical contributions rather than theoretical analysis. While this is the case, our work targets a **highly challenging and practical problem**, cross-domain few-shot object detection, motivated by the limitations of existing approaches, which either require retraining, rely on domain-specific designs, or lack scalability. Facing such a challenging task, we contributed a **novel, effective, training-free, model-agnostic framework** that addresses image generation across domains through the principle of “fix the foreground, adapt the background.” This idea is not only conceptually new but also practically significant.  \nFurthermore, we would like to highlight though without mathematical analysis, we extensively validate our method through: \n\n-   **Effective and widely validated** experiments across diverse benchmarks (CD-FSOD, RS-FSOD, Camouflaged FSOD), and across 8 distinct target domains. \n-   **Comprehensive and detailed ablation studies** and analysis demonstrating the contribution of each component. \n-   **Completely training-free**, enabling easy integration into existing detection pipelines without additional overhead.\n\nWe believe this combination of **innovative design, strong empirical results, and practical feasibility** also provides valuable insights and a robust solution for the community, complementing theory-driven research.\n\n# W2: Dependence on Quantity of Retrieval DB and Generalization.\n\nWe acknowledge the concern and would like to clarify that the domains we tested already include **drastically different scenarios**, such as fine-grained insects (ArTaxOr), cartoon-style drawings (Clipart1k), aerial remote-sensing imagery (DIOR, NWPUVHR-10), underwater cases (UODD, DeepFISH), and industrial textures (e.g., NEU-DET), which are far from the natural image distribution of COCO. Despite this large gap, our method consistently outperforms strong baselines, other competitors, demonstrating robustness even when the retrieval database and target domain differ significantly.   \n\nFurthermore, upon question, we conducted additional experiments under three conditions: 1) COCO as DB but with reduced category numbers, 2) NEU-DET (non-general domain) as DB, and 3) also miniImageNet as DB (originally reported in Tab.6, Appendix)\n\nFrom the below results, we show that: 1) more broader category coverage improves performance, but even with only 1 COCO category, we still clearly enhance the results.  2) Even when the source domain (e.g., NEU-DET) is far from COCO, Domain-RAG still delivers strong gains through its retrieval-conditioned generation and style adaptation modules, 3) As discussed in L927-L935, mini-ImageNet also works as DB, which is also easy to obtain. \n\nFrom these results, we could conclude that our Domain-RAG enhances the baseline performance regardless of the database, solidly validate our effectiveness.  And, the selection of more general-purpose domains e.g., COCO and ImageNet are widely available, thus require no special design, making our approach **practical and scalable**. \n| Method              | DB                   | ArTaxOr | Clipart1k | DIOR | FISH | NEU-DET | UODD | Avg   |\n|:--------------------|:---------------------|--------:|----------:|-----:|-----:|--------:|-----:|------:|\n| Base GroundingDINO   | -                    |    26.3 |      55.3 | 14.8 | 36.4 |     9.3 | 15.9 |  26.3 |\n| Domain-RAG          | COCO-1classes        |    50.1 |      55.0 | 15.7 | 36.6 |    12.0 | 16.0 |  30.9 |\n| Domain-RAG          | COCO-5classes        |    51.0 |      55.1 | 16.6 | 36.7 |    11.9 | 17.5 |  31.5 |\n| Domain-RAG          | COCO-20classes       |    53.0 |      56.2 | 16.2 | 37.0 |    12.2 | 18.9 |  32.3 |\n| Domain-RAG          | **COCO-80classes (Ours)**|    **57.2** |      **56.1** | **18.0** | **38.0** |    **12.1** | **20.2** |  **33.6** |\n| Domain-RAG          | NEU-DET              |    49.8 |      55.2 | 16.4 | 37.0 |    12.0 | 16.1 |  31.1 |\n| Domain-RAG          | miniImageNet (Tab.6) |    55.6 |      53.2 | 15.6 | 38.0 |    14.0 | 16.2 |  32.1 |\n\n# Q1: Additional Controls or Safeguards to Mitigating the Foreground Leakage? \nThanks, we appreciate the discussion. We also considered this question ourselves during the method development and Reviewer-wq5x (Q3) also asked about this.  Here are our responses: \n\nBoth we authors and Reviewer-wq5x consider adding post-filtering as a safeguard; however, it is non-trivial to achieve a good and especially efficient filtering strategy. Specifically,\n\n1) Directly applying ZSL inference of existing detectors for foreground detection on CDFSOD is weak, making it difficult for a pseudo-detector to accurately identify leakage. While if we want to apply the trained detector, e.g., trained Domain-RAG (based on GroundingDINO), it will largely burden the pipeline to: data augmentation → train model → filter → train model again. \n\n2) We also consider applying VLMs (e.g., GPT-4o) to help with filtering. However, they also struggle to meet our expectations.   In the meantime, it will also increase the time cost and complexity.\n\nBesides, we analyzed leakage cases and found that among all generated images in the CDFSL, for example,  only very few examples exhibited a foreground leakage issue. For example, 1.3% (4/290) under 1shot. \n\nConsidering these trade-offs, and given the extremely low frequency of leakage (1.3%), we decided not to introduce post-filtering as it would not significantly impact overall performance while violating the method’s training-free and lightweight design.\n\n# Q2: Adapt to Zero-Shot Scenarios?\n\nThanks. Our method assumes access to target-domain foreground samples, which are provided in any few-shot tasks. Fully zero-shot scenarios, where no target images are available, fall outside this scope.  We will better clarify in the revised version. \n\n# Q3: Selection of Hyperparameter (λ₁, λ₂).\n\nThe 1-shot results of different blending coefficients (λ₁, λ₂) are provided below. Experiments show that λ₁ = 1 and λ₂ = 0.8 achieve the best balance. When λ₂ = 1, we find that  λ₁ = 1 performs best. During testing, we slightly reduced λ₂ to 0.8 because setting it too low (e.g., λ₂ = 0.5) distorts semantics and degrades generation quality. This design ensures that domain-specific cues are preserved while still incorporating context from the retrieval database.\n\n| Hyperparameters (λ₁, λ₂) | (0.5,1) | (0.8,1) | (1,1)  | **(1,0.8)** | (1,0.5) |\n|:-------------------------|--------:|--------:|-------:|:-----------:|--------:|\n| **ArTaxOr Performance**  |   51.9  |   52.3  |  53.1  |  **57.2**   |   54.7  |\n\n# Q4: Caption of Figure 3 contains a typo. (a) → a).\nThanks for your careful review. We will fix it!\n\n# Q5: Effect of Augmented Sample Count per Category.\nThanks, we conducted the ablation of different numbers of augmented samples per category (n) in Tab.8, Appendix (where “m” was incorrectly written instead of “n”; this will be corrected).  n is selected as 5 according to the ablation results. Note that once we select the best n, we keep it the same for all 3 tasks and 8 targets, without manually tuning it on each target. \n\nWe hope that our response adequately addresses your concerns. We are happy to provide more information during the discussion phase."}, {"review_id": "FApAleOI0q", "reviewer": "Reviewer_Dhsv", "summary": "This paper aims to tackle the task of Cross-Domain Few-Shot Object Detection (CD-FSOD), which involves detecting new-class objects with a limited number of labels in unseen domains. The authors find that common data augmentation methods, such as copy-paste, used in previous works, cannot generate realistic samples for this task. They then propose a comprehensive data framework, named Domain-RAG, with a training-free nature, to augment data for CD-FSOD. This framework consists of three stages. It decouples the image into foreground and background, and retrieves similar images in a dataset, based on which it further simulates new backgrounds. Finally, the proposed method combines the objects and simulated background into a new image, serving for the model training. Experiments on three FSOC datasets show that the proposed method can achieve noticeable performance gains.", "questions": "- Considering the usage of several generative models, how long will it take to generate one image? This is critical for dataset scaling.\n- It is suggested to report the results of baseline GroundingDINO with and without fine-tuning. \n- How about the efficiency of Domain-RAG in comparison with other related works?\n- The proposed method uses a stronger backbone. How will the performance be using consistent backbones with other counterparts?\n- Will the proposed method improve the generic few-shot object detection without other domains? I did not find the specific designs for the studied cross-domain setting. Please give some comparisons with state-of-the-art methods.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n- The paper is easy to follow\n- The proposed method can synthesize high-quality samples with diverse backgrounds, which is useful for some design perception tasks. The quality of the generated samples in the Appendix seems satisfactory.\n- The method is training-free, fully using existing foundational models. \n- The experiment performance is state-of-the-art; The ablation of different types of generation designs is interesting.\n\nWeakness\n- The proposed method potentially has general usage instead of being tailored for this specific task (CD-FSOD). The studied scope is limited.\n- Some experiment details need to be clarified. Besides, due to the extra usage of data and models, the comparison may be unfair\n- The model efficiency, such as parameters and inference speed, of the proposed and compared methods should be reported", "was_revised": true, "final_justification": "Thanks for the detailed response, which has addressed my concerns. Hence, I am happy to keep my original positive score.", "rebuttal": "Thank you very much for the positive and constructive feedback! We really appreciate that you consider our work “easy to follow”, “training-free”, “can synthesize high-quality samples”, and delivering “state-of-the-art results and interesting ablations”. Below, we address the concerns raised.\n\n# W1&Q5:  Potential General Usage Instead of being Tailored for CD-FSOD. \nThanks. We appreciate this constructive suggestion. However, we would also like to highlight that: \n\n1) We focus on CD-FSOD mainly due to its unique and challenging characteristics: significant domain shift and limited training examples.  These two challenges make almost all the prior image generation can’t work well.  In contrast, our method perfectly addresses their limitations with our training-free, retrieval-based augmentation strategies, showing a clear performance boost over other methods. That said, if this is not the case with CD-FSOD, we most likely could propose new solutions with better solutions, for example, finetuning when the examples are not few-shot. \n\n2) Besides, in addition to the CD-FSOD it alone, in our paper, we also considered and conducted experiments on other FSOD tasks: in Tab.2, we studied the standard Remote-Sensing FSOD (which falls within general FSOD instead of CD-FSOD), and the adapted Cross-Domain Remote-Sensing FSOD; in Tab.3, we studied the CamouflageFSOD. These broader experiments demonstrate our effectiveness beyond CD-FSOD.\n\nFor more broader scope, e.g., general object detection, we agree our method has potential usage but also we think the current work might not the optimal form, so we will take the suggestion and explore it as our future work.\n\n# W2: Experiment Details and Fairness Due to Data Usage and Models.\nThanks. For the experiment details, in addition to Setups and Implementation Details provided in Sec.4 (L204-L220), we also provide more details in Sec.A.1(Appendix), including more details on the proposed method (Sec.A.1.1), more details on training (Sec.A.1.2), and more ablation studies (Sec.A.1.3).\n\n\nFor the Fairness issue, on the data side, we clarify that: though we use COCO as a database to augment new images, COCO itself is defined as the source in the default CD-FSOD setting, and we don’t break this formulation. It fundamentally should be regarded as different ways of using the source data. Additionally, 1) the comparison between ours (33.6, Tab.1) and other competitors, e.g., ETS (27.8, Tab.1),  Copy-Paste (27.9, Tab.4), Foreground Aug. (28.1, Tab.4), and Background Aug. (29.5, Tab.4) is fair: we share the exact same baseline and training configuration, and all of them are data augmentation-based approaches. For example, ETS proposes a mixture of 6 various augmentation strategies during training;  2) the ablation experiments on Fig.3(a) and Tab.5 (Appendix) all use COCO as DB while without applying the complete Domain-RAG method. We believe these results confirm that the improvements stem from our pipeline design rather than merely the introduction of synthetic data.  \n\nOn the model side, since we aim to build a training-free augmentation, the introduction of extra generation models, e.g., FLUX, is unavoidable. However, we highlight: 1) As stated before, the baseline (GroundingDINO) is the same as others; 2) All the extra introduced models, e.g., FLUX, are only used for the generation; during the inference time, only the single base GroundingDINO is adopted, which is also the same as others.  \n\n# W3&Q1&Q3: Comparison of Model Efficiency, such as Parameters and Inference Speed.\n\nThanks, our method builds on GroundingDINO without modifying any model architecture or training/inference process on the target support sets. Therefore, both parameter count (233.00 M) and inference speed (~0.22 seconds per image) are the same as those of competitors that are built on the same GroundingDINO, e.g., GroundingDINO itself, EST, and our Domain-RAG. \n\nIn addition, upon request, we further provide the computation time needed for our image generation below. We emphasize that: 1) the generation indeed takes time, while it is a long-standing issue in the whole generation community. 2) The generation period introduces no additional cost during inference. 3) The current bottleneck of CD-FSOD lies in the model performance, due to its huge challenges, while our method provides practical and effective solutions for enhancing existing detectors for CD-FSOD. \n\nThese together make our method still scalable, especially under low-shot cases, where only a few query images are needed to be augmented. \n\n\n| Method              | Background Retrieval | Background Generation | Image Composition | Total Time (s/img) | Avg. mAP (1-shot) |\n| :------------------ | :------------------: | :-------------------: | :---------------: | :----------------: | :---------------: |\n| Baseline            |          ✗           |           ✗           |         ✗         |         ✗          |       26.3        |\n| Copy-Paste          |        ✓ (0.1)        |           ✗           |      ✓ (0.1)      |       ~0.2         |       27.9        |\n| Foreground Aug      |        ✓ (0.1)        |           ✗           |      ✓ (8.0)      |       ~8.1         |       28.1        |\n| Background Aug      |          ✗           |        ✓ (3.0)         |      ✓ (15.0)     |       ~18.0        |       29.5        |\n| Domain-RAG (Ours) |     ✓ (0.14)      |     ✓ (27.0)       |    ✓ (28.0)   |    ~55.14      |     **33.6**      |\n\n# Q2: Grounding-DINO with/without Fine-tuning. \nThanks, we provide a comparison of GroundingDINO under different training strategies: zero-shot, 1-shot fine-tuning, and with Domain-RAG.\n\nWe notice that: \n-   Zero-shot GroundingDINO shows limited generalization, especially on challenging datasets (e.g., ArTaxOr, NEU-DET).\n-   Fine-tuning with 1-shot improves results moderately.\n-   Domain-RAG with fine-tuned GroundingDINO achieves consistent gains across all datasets, with a +30.9 mAP increase on ArTaxOr and +4.3 mAP on UODD compared to 1-shot GroundingDINO.\n\nThis demonstrates that GroundingDINO is a strong baseline and Domain-RAG helps improve its generalization in few-data, cross-domain scenarios further. \n\n| Method                          | ArTaxOr | Clipart1k | DIOR | DeepFish | NEU-DET | UODD | **Avg.** |\n| :------------------------------ | ------: | --------: | ---: | -------: | ------: | ---: | -------: |\n| GroundingDINO (ZSL)             |    11.3 |      54.1 | 15.6 |     35.3 |     3.1 | 13.9 |     22.2 |\n| GroundingDINO (1-shot) (Tab.1)  |    26.3 |      55.3 | 14.8 |     36.4 |     9.3 | 15.9 |     26.3 |\n| **Domain-RAG (Ours) (Tab.1)**   |  **57.2**|    **56.1**|**18.0**|   **38.0**|  **12.1**|**20.2**|   **33.6** |\n\n# Q4:  A Stronger Backbone Used.\nAs mentioned in W2, we conducted controlled experiments using the same backbone and training setup as competitors (e.g., ETS) to ensure a fair comparison.\n\nDomain-RAG consistently outperforms these baselines, showing that the performance gain comes from our augmentation method rather than differences in model capacity. To further demonstrate the generality of Domain-RAG, we applied it to DEViT-FT and CD-ViTO. As shown below, both “DEViT-FT +  Domain-RAG” and “CD-ViTO + Domain-RAG” yield consistent improvements across all datasets.\n\n| Method                | ArTaxOr | Clipart1k | DIOR | DeepFish | NEU-DET | UODD |\n| :-------------------- | ------: | --------: | ---: | -------: | ------: | ---: |\n| DeViT-FT              |    10.5 |      13.0 | 14.7 |     19.3 |     0.6 |  2.4 |\n| DeViT-FT + Ours   |    10.4 |    17.0 | 15.7 |     19.1 |     2.4 |  3.0 |\n| CD-ViTO               |    21.0 |      17.7 | 11.7 |     20.3 |     3.6 |  3.1 |\n| **CD-ViTO + Ours**    |    **22.5** |    **20.1** | 15.7 |    **22.4** |     3.6 |  3.3 |\n\nThese results confirm that Domain-RAG improves detection performance across different model architectures, supporting its applicability as a general-purpose augmentation strategy. We will include these in the revised version.\n\n\nWe hope that our response adequately addresses your concerns. We are happy to provide more information during the discussion phase."}]

Author rebuttal:

Meta-review: This paper works on the problem of Cross-Domain Few-Shot Object Detection (CD-FSOD). It introduces Domain-RAG, a retrieval-guided and training-free image compositional generation framework that synthesizes domain-aligned backgrounds that preserve original foreground objects, and new, stylistically and semantically matched backgrounds. Extensive experiments demonstrate that Domain-RAG achieves consistent improvements over existing methods.

The paper received three borderline accepts and one borderline reject initially. The main strengths include: 1) the method is well designed and motivated, and can be adaptable and complementary other methods; 2) the results look promising with extensive evaluation; 3) the paper is well written and easy to follow, and acknowledges the limitations. However, the main weaknesses include: 1) missing some key discussion; 2) missing ablation and comparison; 3) the proposed method is on the top of existing techniques, and the novelty is somehow limited; 4) the paper is of empirical findings without theoretical analysis and insights; 5) the proposed method is general and should be tested in other applications; 6) missing efficiency/speed analysis. 

After the rebuttal, almost of all concerns of the reviewers were well addressed, and they remained positive of this paper. The AC agrees to accept this paper. The authors should follow the reviewers' feedback to improve the final version.
Decision: Accept (poster)