Title: Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Cross-Domain Few-Shot Object Detection;  RAG; Few-Shot Learning;
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nSee Questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1: 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)?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFew-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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}