RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection

• URL: http://arxiv.org/abs/2511.20989v1
• Date: Wed, 26 Nov 2025 02:42:52 GMT
• Title: RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection
• Abstract summary: Ref-COD segments specified camouflaged objects in a scene by leveraging a small set of referring images.<n>Current systems adopt a dual-branch design that requires reference images at test time.<n>We introduce a Ref-COD framework that distills references into a class-prototype memory during training and synthesizes a reference vector at inference.
• Score: 29.264612094775988
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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