Title: DON’T NEED RETRAINING: A Mixture of DETR and Vision Foundation Models for Cross-Domain Few-Shot Object Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: cross-domain few-shot object detection

foundation model
Abstract: Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to generalize to unseen domains by leveraging a few annotated samples of the target domain, requiring models to exhibit both strong generalization and  localization capabilities. However, existing well-trained detectors typically have strong localization capabilities but lack generalization, whereas vision foundation models (VFMs) generally exhibit better generalization but lack accurate localization capabilities. In this paper, we propose a novel Mixture-of-Experts (MoE) structure that integrates the detector's localization capability and the VFM's generalization by using VFM features to improve detector features. Specifically,  we propose Expert-wise Router (ER) that selects the most relevant VFM experts for each backbone layer, and Region-wise Router (RR) that emphasizes foreground and suppress background. To bridge representation gaps, we further propose Shared Expert Projection (SEP) module and Private Expert Projection (PEP) module, which align VFM features to the detector feature space while decoupling shared image feature from private image feature in the VFM feature map. Finally, we propose MoE module to transfer the VFM’s generalization to the detector without altering the detector original architecture. Furthermore, our method extend well-trained detectors for detecting novel classes in unseen domains without re-training on the base classes. Experimental results on multiple cross-domain datasets validate the effectiveness of our method.

DON'T NEED RETRAINING: A Mixture of DETR and Vision Foundation Models for

Abstract

Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to generalize to unseen domains by leveraging a few annotated samples of the target domain, requiring models to exhibit both strong generalization and localization capabilities. However, existing well-trained detectors typically have strong localization capabilities but suffer from limited generalization, whereas vision foundation models (VFMs) generally exhibit better generalization but lack accurate localization capabilities. In this paper, we propose a novel Mixture-of-Experts (MoE) structure that integrates the detector's localization capability and the VFM's generalization by using VFM features to improve detector features. Specifically, we propose Expert-wise Router (ER) that dynamically selects the most relevant VFM experts for each backbone layer, and Region-wise Router (RR) that emphasizes foreground and suppress background. To bridge representation gaps, we further propose Shared Expert Projection (SEP) module and Private Expert Projection (PEP) module, which align VFM features to the detector feature space while decoupling shared image feature from private image feature in the VFM feature map. Finally, we construct MoE module to transfer the VFM's generalization to the detector without modifying the original detector architecture. Furthermore, our method extend well-trained detectors for detecting novel classes in unseen domains without re-training on the base classes. Experimental results on multiple cross-domain datasets validate the effectiveness of our method.

1 Introduction

Few-Shot Object Detection (FSOD) aims to detect objects of novel classes with a few labeled support data. Existing FSOD methods [1, 2, 3, 4, 5, 6, 7, 8] have achieved notable progress in generalizing to various in-domain novel categories. However, these methods often struggle with domain shifts, where training and testing data originate from different domains. This challenge underscores the significance of Cross-Domain Few-Shot Object Detection (CD-FSOD). CD-FSOD aims to generalize object detection models to detect novel classes in unseen domains by using a few training samples. This challenging task typically requires model to combine strong generalization and accurate localization capability.

Existing well-trained object detection methods [9, 10, 11, 12, 13, 14, 15] usually excel at localizing and recognizing seen objects but struggle to generalize to unseen categories or knowledge. To address this problem, previous CD-FSOD methods [16, 17, 18] mainly adopt a two-stage training paradigm that first involves base training on a large-scale dataset of base classes, follow by novel fine-tuning

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on a small dataset of novel classes. However, these models are typically redesigned for CD-FSOD and retrained on base classes which brings substantial computational and time costs, as shown in Figure 1. Moreover, they fail to fully utilize the well-trained powerful object detection models, e.g. , DETR-based methods [19, 20, 21, 22]. Thus, we propose a novel CD-FSOD paradigm by extending well-trained in-domain object detection methods to detect unseen classes in new domains without retraining on the base classes. Our key idea is to leverage the powerful vision foundation models (VFMs) [23, 24] to equip well-trained DETR models with powerful domain generalization ability.

Vision foundation models [26, 27] have demonstrated excellent generalization to new domains, powered by sophisticated architectures and large-scale pretraining datasets. However, VFMs typically lack accurate localization capabilities due to the lack of bounding box annotations in their pretraining datasets. Consequently, directly applying VFMs to object detection generally results in suboptimal performance. To address this problem, existing VFM-based CD-FSOD methods [18, 28] usually modify the detector structure to integrate VFMs and retrain on the base data to achieve precise localization capabilities, which incurs substantial computational and time costs.

It is essential to fully utilize the VFM's generalization capabilities and the well-trained object detection model's localization capabilities while reducing computational and time costs. Thus, instead of altering the structure of existing detection models, we introduce a flexible framework based on a Mixture-of-Experts (MoE) [29] architecture to integrate detector with VFM. Our method enables well-trained detectors to achieve both strong localization and generalization capabilities, without retraining on base class. Specifically, we adopt Detection Transformer (DETR) as our detection framework due to its superior localization capabilities and leverage VFM features as experts to improve the detector's representations by aggregating both detector and VFM features.

Overall, our method focuses on two parts: VFM expert feature selection and VFM-detector feature fusion . Since detector features at different layers contain varying levels of semantic information, each backbone layer generally requires distinct VFM features for effective guidance. Accordingly, we propose Expert-wise Router (ER) to select appropriate VFM features based on image features from different backbone layers. Meanwhile, to highlight the foreground regions and suppress irrelevant background in the VFM feature map, we propose the Region-wise Router (RR) module which generates region-wise gating weights to reweight different spatial regions in the VFM expert features. The ER and RR modules respectively generate expert-wise and region-wise gating weights, dynamically prioritizing the most relevant VFM expert features for detector features at different layers and effectively suppressing irrelevant background in the VFM expert features.

After selecting expert features, it is essential to ensure effective feature fusion between VFM expert features and detector features. Due to the differences in both channel and spatial dimensions between VFM expert features and detector features, it is necessary to project VFM features into the detector feature space. However, assigning a dedicated projection layer to each expert feature will introduce a large number of additional parameters, which increases the computational cost. In contrast, using a single shared projection layer fails to capture the diverse projection requirements of different expert features. To address this issue, we propose the Shared Expert Projection (SEP) and Private Expert Projection (PEP) modules to project the VFM features to the detector feature space with minimal parameters. Each expert feature contains shared image features, such as object shape. Additionally, since each expert feature may focus on different regions of the object, it also includes private image features. Based on this characteristic, we decouple the shared and private features of each expert feature and project them using separate modules. Specifically, we use the SEP module to project

shared features across all expert features, while using the PEP module to project the private features that are unique to each expert feature. This design minimizes the number of parameters required for feature projection and preserves detailed features from different object regions.

Compared with existing methods that adopt the foundation model as a learnable backbone [30, 31, 32] or a frozen backbone [33, 34], our method offers the following advantages:

Maintaining strong localization capability. Our method uses VFM features as experts to enhance detector backbone features, rather than using the VFM as the detector backbone directly. This design avoids transferring the VFM's limited localization capability to the detector.

No retraining requirement on base classes. We introduce only a few trainable parameters to fuse VFM and detector features. Therefore, the model can be directly finetuned on downstream tasks without retraining on base classes.

High extensibility. By treating the VFM as an expert model independent of the detector, our method avoids modifying the detector structure and can be easily transferred to other well-trained object detection models.

2 Related Works

DETR and Its Variants. Detection Transformer (DETR)[25] initially proposed an end-to-end object detector based on the Vision Transformer architecture. Many subsequent studies [35, 36] have improved DETR from various perspectives, such as accelerating convergence speed [37, 38, 20, 39], improving the matching strategy [38, 40], and applying advanced query learning methods [41, 21, 42, 43]. Although these methods demonstrate strong performance, DETR and its variants typically struggle on CD-FSOD tasks because they are trained on base classes and thus fail to extract robust representations for target domain images, impairing DETR's generalization capabilities. In this work, we propose a MoE framework to integrate DETR with vision foundation model. Specifically, we leverage vision foundation model's features as experts to improve the detector's features, enabling the detector to inherit the strong generalization of vision foundation model.

Cross-Domain Few-Shot Object Detection. Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to enable object detection models to detect novel classes in unseen domains by using only a few training samples. Existing FSOD methods can be categorized into meta-learning-based [44, 34, 45], fine-tuning-based [46, 47, 48, 49], and data-enhancement-based [50, 51, 52, 53] approaches. Although these methods perform well in standard FSOD tasks, their performance generally degrades in CDFSOD settings due to substantial domain gaps. Several methods [54, 55] specifically address CD-FSOD challenges. For instance, AcroFOD [16] uses domain-aware augmentation to reduce domain gaps. OA-FSUI2IT [17] leverages a few unlabeled target-domain samples to generate cross-domain images for feature alignment. CD-ViTO [18] improves inter-class discrimination using DINOv2 prototypes. ETS [56] uses a two-step augmentation strategy for foundation model adaptation. CDFormer [57] introduces transformer-based modules to distinguish object-background and object-object features. IFC [58] uses learnable instance feature caches for robust prototypes. These methods generally require modifying the original detector's structure and retraining on the base classes. In this work, we propose a novel Mixture-of-Experts (MoE) structure to aggregate VFM features and detector features with a few parameters. Our method treats the VFM as an expert model independent of the detector, avoiding modifications to the detector architecture and reducing the computational and time cost of retraining.

Vision Foundation Model. Current vision foundation models (VFMs) are primarily trained under two paradigms: supervised learning and self-supervised learning. Supervised learning model [59, 60] commonly relies on high-quality labeled datasets for pretraining. These models often exhibit strong generalization, demonstrating excellent performance even without fine-tuning based on downstream tasks. However, large-scale, high-quality labeled datasets are difficult to obtain due to the high cost of manual annotation. To address this problem, self-supervised learning models [61, 62, 63, 64, 65, 66] leverage techniques such as contrastive learning [67, 68] and masked image modeling [69, 70] to train on unlabeled data, reducing reliance on labeled datasets and improving image understanding through greater data diversity. However, the lack of accurate bounding boxes limits the localization capabilities of vision foundation models. When the vision foundation model is directly used as the backbone for object detection tasks, its weak localization capability is transferred to the detector.

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To overcome this limitation, we use VFM features as experts to improve the detector backbone features, rather than directly replacing the detector's backbone. This approach avoids modifications to the detector architecture and preserves the detector's localization capability. Additionally, since our method does not modify the detector structure, it can be easily applied to other detector architectures.

3 Method

3.1 Method Overview

To fully exploit the VFM's generalization for CD-FSOD while preserving the detector's strong localization capability, we propose a novel MoE framework that leverages VFM features to improve detector representations without modifying the original detector architecture, as shown in Figure 2. It consists of two key components: the expert routing module and the expert projection modules. Specifically, given VFM expert features { F n d } N n =1 ∈ R B × C d × H d × W d and detector feature F l ∈ R B × C × H × W from the l -th detector backbone layer, where B denotes the batch size, N corresponds to the number of expert features, C, C d represent the channel dimension of detector feature and VFM expert feature, H × W,H d × W d represent the spatial size of detector feature and VFM expert feature, the Expert-wise Router (ER) module is used to generate expert-wise gating weights base on F l to select suitable VFM expert features for current detector feature. Simultaneously, the Region-wise Router (RR) module is used to generate region-wise gating weights based on F l to suppress irrelevant background regions in the selected VFM expert feature. Then, the Shared Expert Projection (SEP) and Private Expert Projection (PEP) modules are applied to address the dimensional mismatch between the VFM features and the detector features. The SEP module is used to project the shared feature contained in expert features. The PEP module is used to project the private feature contained in each expert feature. After applying expert projection, the projected VFM features are denoted as { F n,l d ′ } N n =1 ∈ R B × C × H d × W d . Finally, the MoE module fuses VFM features and detector features to obtain F l f ∈ R B × C × H × W as the input for the next layer of the detector backbone.

3.2 Expert Routers

Expert-wise Router. Detector features at different layers contain different levels of image features. Shallow layers primarily focus on low-level feature such as edges and color, and deep layers focus on high-level semantics such as object shape and category. Therefore, the importance of each VFM expert feature varies across different layers of the detector backbone. Inspired by the routing strategy of MoE, we propose the expert-wise router to select the most appropriate VFM expert features for detector features at different backbone layers. Specifically, for the l -th layer detector feature F l , we first apply

global average pooling along the spatial dimensions to aggregate spatial information of detector feature and obtain spatial-wise aggregated feature vector f g = 1 H × W ∑ H i =1 ∑ W j =1 F l (: , : , i, j ) . Then, the tensor f g is fed to a learnable fully connected layer θ e to aggregate channel information of detector feature and obtain channel-wise aggregated feature vector f ′ g = θ e ( f g ) . Finally, a softmax function is applied to the channel-wise aggregated feature vector f ′ g along channel dimension to obtain the expert-wise gating weight G e = softmax ( f ′ g ) .

The expert-wise router generates expert-wise gating weights to select the most suitable VFM expert features for different backbone layers. These VFM expert features typically exhibit high similarity to the detector features, indicating they are more likely to focus on similar levels of image semantics. Therefore, the expert-wise router can effectively improve the detector's feature of different layers.

Region-wise Router. Different regions in the VFM feature maps exhibit varying importance for the detector. Foreground regions are typically more informative for object detection and background regions often contain more task-irrelevant image features. To filter out irrelevant background regions and highlight foreground objects, we propose the region-wise router to generate region-wise gating weights for different regions of the VFM expert feature maps. Specifically, the l -th layer detector feature F l is fed to a learnable fully connected layer θ r along channel dimension to obtain projected tensor f r = θ r ( F l ) . The projected tensor f r is then normalized using a softmax function to generate the region-wise gating weights G r = softmax ( f r ) .

The region-wise router generates region-wise gating weights to reweight different regions of the VFM feature map. Compared with VFM, the detector can precisely localize foreground regions because of its stronger localization capability. Therefore, The region-wise router generates region-wise gating weights based on the detector features to highlight foreground regions.

3.3 Expert Projections

Since VFM expert features and detector features differ in both spatial and channel dimensions, it is necessary to project VFM expert into the detector feature space to align their feature dimensionality. However, due to the large number of VFM experts, assigning a dedicated projection layer to each VFM expert feature would introduce substantial parameters and degrade training efficiency. In contrast, using a single shared projection layer reduces parameter overhead but fails to accommodate the diverse projection needs across VFM expert features. To address this problem, we propose the Shared Expert Projection (SEP) module and Private Expert Projection (PEP) module. Each expert feature contains both shared information, e.g. , object shape, and private information, e.g. , fine-grained details from different object regions. The SEP module is used to project shared information across all VFM expert features, and the PEP module is used to project private information specific to each VFM expert feature.

Shared Expert Projection. The shared expert projection module enables the model to effectively project shared image feature across different expert features. Specifically, for the i -th VFM expert feature F i d , we apply a shared linear projection layer θ s ∈ R C d × C s to all expert features, where C s = m -1 m C , m is hyperparameter that control the channel dimensions of the shared expert projection module transformation. This produces the shared projection component F i s = F i d · θ s .

Private Expert Projection. The private expert projection modules enable the model to project private image features contained in each VFM expert feature. Specifically, each expert feature applies its own private projection layer θ i p ∈ R C d × C p to project its private image features, where C p = 1 m C . The private projection component F i p can be obtained as F i p = F i d · θ i p .

The projected VFM expert feature F i d ′ is constructed by concatenating F i s and F i p along the channel dimension. After projecting the VFM expert features along the channel dimension, we resize their spatial size via bilinear interpolation to match the spatial resolution of l -th detector backbone feature. The final VFM expert features at l -th detector backbone layer are denoted as { F n,l d ′ } N n =1 .

3.4 Mixture of Experts

Given the projected expert features { F n,l d ′ } N n =1 and their corresponding expert-wise and region-wise gating weights for the l -th detector backbone layer, the final feature fusion is performed by our MoE

module:

where ⊛ denotes tensor broadcast multiplication, and α and β are weighting factors, set to α = 0 . 5 , β = 0 . 5 in our experiment. F l f denotes the output of the l -th detector's backbone layer. Simultaneously, it also serves as the input to layer l +1 . We repeat this process across all backbone layers until obtaining the final layer backbone features, which are then fed into the subsequent detector components to obtain the object detection results.

4 Experiments

We adopt the DINO detector [20] which is trained on the COCO source domain dataset as our baseline and use the self-supervised foundation model DINOv2 [27] as our expert model. Our method is directly applied to the publicly available DINO with a ResNet50 [71] backbone, without any additional re-training on the source domain dataset. For fine-tuning, we employ the AdamW optimizer with a learning rate of 2e-3. Following the benchmark in previous work [18], we evaluate our method on six datasets with distinct domain shifts: ArTaxOr (insect images), Clipart1k (hand-drawn cartoon image), DIOR (remote sensing imagery), DeepFish (underwater fish images), NEU-DET (industrial defect images), and UODD (marine organism images). For the 1/5/10 shot task settings, we train our model for 400, 800, and 1200 iterations, respectively. All experiments are conducted on four NVIDIA RTX 4090 GPUs. We rename the detector DINO as DINO DETR in the following.

4.1 Quantitative Results

Comparison with State-of-The-Arts. As shown in Table 1, we compare our method with typical CD-FSOD [18, 54], ViT-based OD [72], and open-set based OD/FSOD methods [73, 28]. Our method outperforms the baseline method DINO DETR, achieving improvements of 11.1/21.9/24.6 mAP on six cross domain datasets under the 1/5/10 shot task settings, respectively. Additionally, compared with the previous state-of-the-art cross-domain few-shot object detection method CD-ViTO,

our method achieves the improvements of 4.3/8.6/10.2 mAP on six cross domain datasets under the 1/5/10 shot task settings.

Comparison with MLLMs and OVMs. As shown in Table 2, we compare our method with multimodal large language models (MLLMs) and open-vocabulary methods (OVMs). Using their open-source code, we conducted fair comparisons on the same dataset. Qwen model [74] and Ferret model [75] obtain results through text-guided visual grounding. Grounding DINO [76] and YOLO-World [77] derive detection results via image-text matching. Our method achieves the highest performance compared with OVMs and MLLMs across six cross domain datasets. Compared with Grounding-DINO and YOLO-World model, our method achieves the improvements of 22.1/26.3 mAP. For Qwen model and Ferret model, our method achieves the improvements of 27.5/36.2 mAP. We argue that models such as Qwen, despite being trained on large-scale image-text datasets, have never seen the novel classes in the cross domain dataset, resulting in weak zero-shot performance. In contrast, our method adopts a cross-domain learning strategy that integrates with vision foundation models, achieving superior performance on cross-domain tasks.

Method Extensibility Analysis. To further validate the strong extensibility of our method, we adapt our method to other DETR models and evaluate their performance under the 10-shot task setting on all datasets. As shown in Table 3, the average performance of all models has been significantly improved. For instance, the performance of DAB-DETR increased from 9.6 mAP to 35.9 mAP. To validate the effectiveness of our method across different detector backbones, we apply our approach to DINO DETR with various backbones and evaluate their performance under the 10-shot task setting on six cross domain datasets. As shown in Table 4, the experimental results demonstrate that our method achieves strong performance across different backbones. Specifically, when integrating our method with a Swin-B [78] backbone, our method performance improves by 2.4 mAP compared to the ResNet backbone, while using a ViT-L [79] backbone leads to an improvement of 4.6 mAP.

Ablation Studies. To assess the contribution of each module, we conduct ablation studies under the 10-shot task setting across six cross domain datasets. As shown in Table 5, adding the SEP module raises the average performance to 34.6 mAP, surpassing the state-of-the-art CD-ViTO. Building on this, adding the PEP module further improves performance to 35.7 mAP, demonstrating that the private projection layer effectively retains rich private image features in the VFM expert features. After introducing the feature projection modules, we further evaluate the performance of the routing modules. Adding the expert-wise routing (ER) module improves the average performance to 37.2 mAP, highlighting the importance of selecting different VFM expert features to guide different detector layers. Adding the region-wise routing (RR) module further boosts performance to 38.2

mAP, demonstrating that the RR module effectively highlights foreground regions in the feature maps and filters out irrelevant background information. Using ER and RR together achieves the best result of 39.8 mAP, confirming their complementarity.

Cross-Domain Generalization Analysis. To validate that the VFMs have strong generalization and the detectors have poor generalization on the cross domain tasks, we fine-tune DINO DETR, DINOv2 and our method on six cross-domain datasets and evaluate their performance. DINOv2 refers to replacing the ResNet50 backbone of original DINO DETR with DINOv2. We use the average precision loss(dAP) caused by false positive samples as our evaluation metric. As shown in Table 6, DINO DETR has the highest dAP values reach 44.45/40.12/35.68 under the 1/5/10-shot task settings, respectively, indicating its poor generalization in cross domain tasks. In contrast, DINOv2 has lower dAP values of 29.73/27.7/30.26, indicating the strong generalization capability. Our method leverages a router module further to filter out irrelevant background region in VFM feature map. As a result, our method achieves the lowest dAP values of 26.47/22.54/17.98 under the 1/5/10-shot settings, respectively. Notably, under the 10-shot task setting, our method reduces dAP by 12.28 compared with DINOv2, validating the stronger generalization capability of our approach.

Cross-Domain Localization Performance Analysis. To validate that the detectors have strong localization capabilities and the VFMs lack accurate localization capabilities, we fine-tune DINO DETR, DINOv2 and our method on six cross-domain datasets and evaluate their performance under the 1-shot task setting. We use the decrease of AP75 relative to AP50 to evaluate model's localization capability. As shown in Table 7, DINOv2 shows the highest drop of 69.35%, indicating limited localization capabilities because its training dataset lacks accurate bounding box annotations. In comparison, DINO DETR exhibits better localization performance with a drop of 48.76%. Our method treats DINOv2 as an independent expert model to improve the detector's generalization rather than using DINOv2 directly as the detector backbone. Therefore, our method avoids the transfer of weak localization capabilities from the VFM to the detector. Experimental results show that our method achieves an attenuation of only 40.41%, demonstrating its effectiveness in maintaining the detector's localization performance.

4.2 Qualitative Visualizations

Method Analysis. To validate our method's effectiveness, we visualize the backbone feature maps. As shown in Figure 3, our method makes the model focus on foreground regions, showing that our router module can effectively filter out the useless background in the VFM feature maps. Additionally, we use t-SNE [80] to visualize backbone features on the DIOR dataset to demonstrate the model's

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classification performance. As shown in Figure 4, our method shows higher intra-class compactness and inter-class separability, further demonstrating the strong generalization of our model.

Expert-wise Routing Mechanism. To illustrate that the importance of expert features varies across backbone layers, we analyze expert-wise gating weights. As shown in Figure 5, different backbone layers employ different VFM feature selection strategies. Deep detector features generally tend to

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select VFM expert feature from the deep VFM layer because they contain similar level image features. However, the deep detector features also select some shallow VFM expert features, showing that low-level image features, like object edges and colors, can also improve the high-level image feature. The VFM feature selection strategy at the same detector layer also changes on the different test datasets. These experimental results indicate that it is necessary to introduce a learnable module to dynamically adjust the VFM feature selection strategy. Therefore, inspired by the routing mechanism in MoE, we propose an expert-wise router method that generates expert gating weights based on detector features to select the VFM expert feature dynamically.

Region-wise Routing Mechanism. The information density is different in different regions of the VFMfeature map. The foreground regions typically have higher information density, and background regions tend to have lower information density. To make the model to focus on the foreground region, we propose a region-wise router to filter background region of the VFM feature map and highlight foreground regions. To demonstrate the effectiveness of our method, we visualize the region-wise gating weights by using heatmap. As shown in Figure 6, after applying our region-wise router, the model focuses on foreground regions and ignores the irrelevant background regions.

5 Conclusion

In this paper, we propose a novel Cross-Domain Few-Shot Object Detection (CD-FSOD) paradigm leveraging a Mixture of Experts (MoE) architecture to integrate the vision foundation model with the well-trained detection model. Our method transfers VFM's generalization to the detector without modifying the original detector structure or retraining on the base class. We introduce expert-wise and region-wise routers to select VFM expert feature and filter irrelevant background regions in the VFM feature map. Additionally, we propose the shared expert projection module and private expert projections module, which decouple the shared and private image feature projections and minimize the parameters introduced by the VFM feature projection. Extensive experiments demonstrate the effectiveness of our approach in improving cross-domain performance.

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A Ablation Study on Different Vision Foundation Models

To evaluate the impact of using different VFMs as expert models in our method, we compare the performance of using DINOv2 [27], CLIP [81], and combination of both as expert models. As shown in Table 8, DINOv2 has better performance than CLIP on cross-domain tasks. We further explore integrating multiple vision foundation models simultaneously to boost feature representation. The integration of DINOv2 and CLIP exceeds the GPU memory limitation, forcing us to reduce the batch size to 1. Experimental results demonstrate that integrating multiple vision foundation model leads to a slight performance improvement, confirming the complementary of different vision foundation models. However, this integration significantly increases training time and memory consumption. Considering the trade-off between performance improvement and computational costs, we ultimately only select DINOv2 as the expert model.

B Ablation Study on DINOv2 Models of Different Parameter Sizes

To evaluate the impact of VFMs with different parameter scales, we use DINOv2-small, DINOv2base, DINOv2-large, and DINOv2-giant as expert models and evaluate method permformance under the 10-shot task setting. As shown in Table 9, DINOv2-giant exceeds the memory capacity of our GPU, forcing us to reduce the batch size to 1 during training. The performance of DINOv2-giant is suboptimal due to its large intermediate feature maps, which require processing a large number of additional parameters. These extra parameters not only hinder effective fine-tuning on downstream tasks but also considerably increase training time. In contrast, DINOv2-large offers a balanced trade-off between parameter size, training time, and performance improvement, delivering the best results within a reasonable computational cost. Consequently, we select DINOv2-large as the expert model.

C Ablation Study on Hyperparameters Setting

In the shared and private expert projection modules, we introduce the hyperparameters m , to control the parameter scales of the shared expert projection module and the private expert projection module. e.g. , F i s = F i D · θ s ∈ R B × C s × H × W , F i p = F i d · θ i p ∈ R B × C p × H × W , where C s = m -1 m C, C p = 1 m C . To determine the optimal configuration, we conduct an ablation study on the values of m. As shown in Table 10, our method shows the best performance when m = 16 .

In the mixture of experts module, we introduce two hyperparameters, α and β , e.g. , F l f = F l + ∑ N n =1 ( α · G n,l e ⊛ F n,l d ′ + β · G n,l r ⊛ F n,l d ′ ) . To evaluate the impact of these parameters, we perform an ablation study on α and β . As shown in Table 11, the results indicate that the optimal configuration is α = 0 . 5 and β = 0 . 5 .

D Ablation Study on Fine-Tuning Strategy

To evaluate the impact of different fine-tuning strategies, we evaluate the performance of four finetuning strategies: Full Finetune, LoRA [82], Partial Finetune and our fine-tuning strategy. As shown in Table 12, All fine-tuning Strategies keep the vision foundation model frozen without updating its parameters. LoRA-based fine-tuning achieves the lowest number of trainable parameters but suffers from limited performance. Partially fine-tuning the model while excluding the backbone introduces a moderate increase in trainable parameters and yields improved results. Full fine-tuning achieves the highest accuracy, yet comes at the cost of significant training overhead and a heightened risk of overfitting in few-shot scenarios. In contrast, our proposed strategy, which fine-tunes only the classification head, regression head and the new proposed module, strikes an effective balance between computational efficiency and detection performance, demonstrating strong generalization in cross-domain few-shot object detection tasks.

E Ablation Study on Feature Aggregation Strategy

To validate the effectiveness and necessity of our proposed MoE-based feature aggregation strategy, we compare our method with four simpler feature aggregation strategies. Specifically, we design and evaluated four variants:

All Stages : Retain all stage PEP modules and replace them with simple MLPs, removing all other modules.

Final Stage : Retain only the final-stage PEP module and replace it with a simple MLP, removing all other modules.

Linearly Adding : Remove all modules and aggregate features at the backbone stage via linear addition.

Learnable Weight : Introduce a learnable weight parameter to aggregate features from different layers of the VFM.

As shown in Table 13, simple feature aggregation strategies lead to a substantial performance decline, thereby validating the efficacy of our approach. Learnable Weight feature aggregation strategy introduces learnable weight parameters as a simple routing network to select and aggregate VFM features, thereby maintaining the MoE structure similar to ours. In contrast, the other three aggregation strategies (All Stages, Final Stage, Linearly Adding) simply aggregate the VFM features into the backbone features without selecting them. Therefore, it outperforms the other simple aggregation strategies. However, it still leads to a decline in performance compared to our method, highlighting the importance of backbone features in guiding VFM feature selection.

F Comparison with Recent Works

Comparison with ETS. We compare our method with ETS [56] on six cross domain datasets. For fairness, we reproduce the experiments using the official ETS code. As shown in Table 14, our method can be directly applied to Grounding DINO without any modification and achieves similar performance to ETS, even though it is not specifically designed for Grounding DINO.

Comparison with Frozen DETR. We compare our method with Frozen DETR [83]. We adopt the same baseline structure as Frozen DETR. Frozen DETR follows a two-stage training paradigm of base training and finetuning. To align the experimental and dataset settings, we skip the base training step, finetuning only on the cross domain datasets. To validate the effectiveness of the method on in-domain datasets, we train our method on COCO for 12 epochs and compare it to the performance of Frozen DETR reported in the original paper. As shown in Table 15, our method consistently outperforms Frozen DETR across six cross domain datasets and the COCO dataset.

G Analysis of Routing Network Based on Detector Feature

To validate the feasibility of using detector features to route VFM features, we visualize the feature maps of detector and VFM using PCA. As shown in Figure 7, the detector shows higher attention in the foreground regions, reflecting its strong localization ability. However, the foreground object feature are unclear, leading to weaker classification performance. In contrast, the VFM extracts clear feature representations but maintains high attention in multiple background regions, indicating its weaker localization ability. Therefore, we leverage the detector's localization ability to guide VFM feature maps in supplementing the missing key features in foreground regions.

DINO DETR

DINOv2

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes incorporating the general representation capabilities of pre-trained vision foundation models as expert knowledge into object detectors, aiming to enhance their detection performance in unseen domains. Specifically, the output features from each layer of the VFM are treated as individual experts. A Mixture-of-Experts-based approach is introduced to dynamically route and modulate the VFM features. Experiments demonstrate that the proposed method achieves performance improvements in CD-FSOD tasks and can be used as a plug-in module for various detectors.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation is clear and meaningful.\n2. The proposed method is simple and effective, and it can be extended to various DETR-style detectors.\n3. The paper provides comprehensive analytical experiments.\n\nWeaknesses:\n1. The paper contains some unclear parts. For example, (1) The feature shapes of DINOv2 differ from those of the backbone (ResNet50) in DINO at various stages, yet the paper uniformly represents them using H×W, which may cause confusion. (2) According to Fig. 5, does the proposed method use all 24 output features from DINOv2-L as expert features but perform feature fusion only at the output features of the four stages of ResNet50? (3) What does DINOv2 refer to in Tables 4 and 5, and how does it work?\n2. There is a lack of comparison with related work. The motivation of the paper is similar to that of Frozen-DETR [1], which also transfers the general representations extracted by the frozen VFM to object detectors. It is recommended that the authors include a comparison with Frozen-DETR.\n3. The method proposed in this paper has been evaluated solely on the CD-FSOD datasets. Its effectiveness on general object detection tasks has yet to be demonstrated.\n\n[1] Fu S, Yan J, Yang Q, et al. Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models[J]. arXiv preprint arXiv:2410.19635, 2024.\n\nQUESTIONS:\nPlease answer the following questions:\n1. Addressing the ambiguities noted in Weakness 1.\n2. Please compare with Frozen-DETR on the CD-FSOD datasets.\n3. Is the proposed method effective on in-domain datasets with sufficient samples, such as COCO? Please supplement the experiments of the proposed method on the COCO dataset and compare the results with Frozen-DETR.\n\nSince the motivation of this paper is similar to that of Frozen-DETR, I am very concerned about the comparison and discussion between the proposed method and Frozen-DETR. This will affect my decision on adjusting the score.\n\nIn addition, I have the following suggestion:\nIn Table 1, † indicates that the methods were fine-tuned on the six datasets. In fact, Distill-cdfsod, CD-ViTO, and the proposed method (Ours) all require fine-tuning. The authors are advised to revise this description to avoid misunderstandings.\n\nLIMITATIONS:\nLimitations are not mentioned.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a cross-domain few-shot object detection method that does not require retraining on the source domain. It directly employs a mixture-of-experts (MoE) architecture, where a visual foundation model (DINOv2) provides expert features, and an object detector (DINO) serves as the router network, complementing each other in terms of generalization and localization capabilities. The router network includes expert-wise and region-wise modules that dynamically and iteratively aggregate coarse-grained and fine-grained visual information from the VFM into the object detector's backbone features at each stage. Expert features are projected from the VFM feature maps through shared and private projection modules, effectively modeling both common and expert-specific knowledge. Experiments show that the proposed method outperforms prior approaches on the CD-FSOD benchmark, demonstrates strong transferability across different object detection backbones, and validates the effectiveness of the introduced router and projection modules through ablation studies.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1) The motivation is clear: VFMs pretrained on large-scale data exhibit better generalization but lack accurate localization capabilities, while object detectors (OD) specialize in bounding box regression but are less robust to domain shifts. \n2) The design is straightforward yet effective, leveraging pretrained models without the need for retraining on base classes. This brings practical value to real-world scenarios, where downstream applications demand quick and performant adaptation. \n3) The method exhibits significant performance advantages over existing approaches on the CD-FSOD benchmark. It also showcases transferability across a variety of DETR architectures.\n\n\nWeaknesses:\n1. Clarity of ablation studies: It is unclear what it means for SEP and PEP to exist without ER and RR. How are the VFM features aggregated in this case? Is there still a gating network, or are the features aggregated in a way that is not conditioned on the backbone features?\n\n2. Comprehensiveness of ablation studies: What is the rationale for not enumerating all possible combinations? Since the projection methods and router mechanisms are orthogonal, there should be 3 (SEP only, PEP only, SEP+PEP) × 4 (ER + RR, ER only, RR only, neither) + 1 (DINO only) = 13 combinations.\n\n3. Lack of simpler baselines: Additional baselines are needed to show the necessity of the MoE structure. Table 3 shows a significant performance boost from DINO to SEP only, suggesting that much of the improvement may stem from the rich visual features provided by DINOv2. Did the authors explore simpler baselines, such as aggregating VFM features using a simple MLP (without conditioning on backbone features), or combining VFM features only at the final stage with the backbone features to justify the iterative design?\n\n4. Notations to be improved/typos to be corrected:\n\n    a) At the start of section 3.4, n seems to be overloaded, can be number of experts, also said to be a hyperpamter in expert   projection module; \n\n    b) F_D^l’, prime looks like indicate another layer l’, which I believe the intention here is still to indicate layer l;\n\n    c) Section 4.1, text description is not consistent with the table. Shouldn’t it be 24.6 mAP improvement on 10-shot for the     proposed method vs  DINO. Shouldn’t it be 9.8 improvement on 10-shot for the proposed method vs CD-ViTO\n   \n5. You may want to cite some more recent work:\n\n     [1] Pan, Jiancheng, et al. \"Enhance then search: An augmentation-search strategy with foundation models for cross-domain few-shot object detection.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n     [2]Meng, Boyuan, et al. \"CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion.\" arXiv preprint arXiv:2505.00938 (2025).\n\n     [3] Huang, Yali, et al. \"Instance Feature Caching for Cross-Domain Few-Shot Object Detection.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\nQUESTIONS:\nAside from the questions raised in the above section, I'd like to gain a deeper understanding of the intuition behind this architectural design. Fundamentally, why can an OD that's sensitive to domain shift reliably route VFM features? I know the authors show the effectiveness of the method through experiments, but theoretically, using the OD features to route VFM features seems to be counterintuitive and ill-posed. Would like to see further clarification/elaboration on this design in the paper.\n\nAlso, it would be great to hear authors' thoughst on why for DINOv2, dAP increase to 30.26 from 27.7 , for 5-shot to 10-shot according to the Table 4.\n\nLIMITATIONS:\nThe authors didn't explicitly list the limitations; it would be helpful for future work in this direction if they could share any limitations they're aware of.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors presented a novel approach for Cross-Domain Few-Shot Object Detection (CD-FSOD) that effectively merges the localization abilities of the well-trained object detector with the strong generalized features learned by Vision Foundation Models (VFMs) to improve the detection performance. This is achieved by the Mixture-of-Experts (MoE) module that dynamically routes and modulates the integration of VFM features into the detector training. The MoE module includes two key components: 1)  a routing mechanism that assigns weights to VFM feature maps for each stage of the detector backbone, and 2) an expert projection (EP) module for aligning VFM features to those of detector features. The routing mechanism creates expert-wise weights and region-wise weights. The expert projection includes Shared and Private projections, which are concatenated to create VFM features aligned with backbone features. The aligned features are then processed by MoE with the help of routing weights. The method proposed in this paper can straightaway extend the capacity to detect novel classes without requiring re-training on the base data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper presents a conceptually simple solution for CD-FSOD by integrating the strong generalizable features of VFMs with the localization capacity of trained detectors to facilitate novel object detection with few-shot training. \n2. Different from common FSOD methods, the technique proposed here doesn't need the detectors to be trained on the base classes. The authors used a pre-trained detector on the COCO dataset to adapt to a diverse set of new domains. \n3. The paper is well written and easy to follow. \n4. This method proposed is general purpose; any VFMs can be integrated into the standard detectors to perform FSOD using the proposed approach.  \n\n\nWeaknesses\n1. It would be great to see results on other detection models, though it is not a significant weakness. A recent paper [1] with the GroundingDINO detector has good detection performance on the same problem settings, but not straightforward to compare with the detector and backbone used in this study.\n2. It is not clear what is captured in the shared information and private information projection from the VFM features. The authors stated that the shared projection enables the aggregation of shared image information across different expert features. And the private projection module allows the model to learn tailored projection strategies for each expert feature. A good visualization is missing to understand this with more clarity.\n\n\n[1] Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection, CVPRw 2025.\n\nQUESTIONS:\n1. In L90, the authors state, \"DETR and its variants struggle on CD-FSOD tasks, primarily due to poor classification accuracy\". Is there a reference to any papers or empirical results to justify this? \n2. Please provide results on other backbones for the results in Table 1, especially with VIT-L/14, as most of the methods listed are using that backbone.\n3. Why does the CLIP backbone give limited improvement compared to the DINO v2?\n\nMinor\n1. Repeated text about DINO DETR comparison in L239-243.\n2. dAP value mismatch between Table 4 and the corresponding text in L248.\n\nLIMITATIONS:\nThe limitations are not discussed in the paper. However, the authors state that the paper has limitations. Please include them in the supplementary materials.\n\nOne obvious limitation is that when the number of layers of the detector and VFM increases, the routing computations increase quadratically.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents  a novel framework for cross domain few shot object detection by combining the detection models with vision foundation model in a mixture of experts (MOE) based method. Leveraging VFM and DETR through MOE framework enables improved generalization without loss of localization. In particular, it introduces a plug-in strategy that keeps a well-trained DETR detector frozen and boosts its generalization by injecting features from a vision foundation model, DINOv2. The proposed strategy introduces expert-wise & region-wise routers dynamically weight which DINOv2 layers and spatial regions matter for each DETR backbone stage as well as projection layers (shared and private) for feature alignment. Experimental study on different domains show improved performance over the counterpart methods\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1)\tThe paper is well motivated and efficiently integrates vision foundation models with DETR architectures.\n\n2)\tThe introduced novelties are in plug-and-play format, making them generalizable to various DETR methods\n\n3) Experiments show consistent improvement over the baselines\n\nWeaknesses\n\n1)\tComputational latency: How does the computational latency of the proposed method compared to counterpart methods\n\n2)\tComparisons: How does the proposed method compared to open vocab methods(e.g. grounding-dino, yolo-world).\n\n3)   How does  the proposed method perfroms compared to multi modal LLMs (MLLMs)  such as Qwen models or Ferret models which are well known with their visual grounding.\n\n4)\tNo limitations are listed in the paper. What are the potential pitfall for proposed method.\n\nQUESTIONS:\nI have listed all my concerns in the \"Strength and Weaknesses section\". Please refer to \"Weaknesses\" for the raised concerns and questions.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}