RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917105399496704 |
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| author | Wu, Yu-Huan Zhu, Zi-Xuan Wang, Yan Zhen, Liangli Fan, Deng-Ping |
| author_facet | Wu, Yu-Huan Zhu, Zi-Xuan Wang, Yan Zhen, Liangli Fan, Deng-Ping |
| contents | Referring Camouflaged Object Detection (Ref-COD) segments specified camouflaged objects in a scene by leveraging a small set of referring images. Though effective, current systems adopt a dual-branch design that requires reference images at test time, which limits deployability and adds latency and data-collection burden. We introduce a Ref-COD framework that distills references into a class-prototype memory during training and synthesizes a reference vector at inference via a query-conditioned mixture of prototypes. Concretely, we maintain an EMA-updated prototype per category and predict mixture weights from the query to produce a guidance vector without any test-time references. To bridge the representation gap between reference statistics and camouflaged query features, we propose a bidirectional attention alignment module that adapts both the query features and the class representation. Thus, our approach yields a simple, efficient path to Ref-COD without mandatory references. We evaluate the proposed method on the large-scale R2C7K benchmark. Extensive experiments demonstrate competitive or superior performance of the proposed method compared with recent state-of-the-arts. Code is available at https://github.com/yuhuan-wu/RefOnce. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection Wu, Yu-Huan Zhu, Zi-Xuan Wang, Yan Zhen, Liangli Fan, Deng-Ping Computer Vision and Pattern Recognition Referring Camouflaged Object Detection (Ref-COD) segments specified camouflaged objects in a scene by leveraging a small set of referring images. Though effective, current systems adopt a dual-branch design that requires reference images at test time, which limits deployability and adds latency and data-collection burden. We introduce a Ref-COD framework that distills references into a class-prototype memory during training and synthesizes a reference vector at inference via a query-conditioned mixture of prototypes. Concretely, we maintain an EMA-updated prototype per category and predict mixture weights from the query to produce a guidance vector without any test-time references. To bridge the representation gap between reference statistics and camouflaged query features, we propose a bidirectional attention alignment module that adapts both the query features and the class representation. Thus, our approach yields a simple, efficient path to Ref-COD without mandatory references. We evaluate the proposed method on the large-scale R2C7K benchmark. Extensive experiments demonstrate competitive or superior performance of the proposed method compared with recent state-of-the-arts. Code is available at https://github.com/yuhuan-wu/RefOnce. |
| title | RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.20989 |