将参考图像提炼为原型记忆，推理时无需真实参考图。
RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection
- 训练时将参考图像压缩成类别原型，测试时用查询生成参考向量。
- 在R2C7K数据集上达到领先性能，优于现有方法。
- 适合资源受限场景，部署简单且无额外数据负担。
指称伪装目标检测（Ref-COD）通过少量参考图像定位场景中的指定伪装物体。尽管有效，现有系统采用双分支结构，需在测试时提供参考图像，限制了部署可行性并增加延迟与数据采集成本。本文提出一种新框架，将参考图像在训练阶段提炼为类别原型记忆，并在推理时通过查询条件化的原型混合生成参考向量，无需任何测试时的参考图像。具体而言，每个类别维护一个基于指数移动平均（EMA）更新的原型，并从查询中预测混合权重以生成引导向量。为弥合参考统计与伪装查询特征之间的表征差距，设计双向注意力对齐模块，自适应调整查询特征与类别表示。所提方法实现了无需强制参考图像的简洁高效的Ref-COD路径。在大规模R2C7K基准上进行评估，大量实验表明其性能优于或媲美近期先进方法。代码已开源：https://github.com/yuhuan-wu/RefOnce。
原文摘要 · Abstract (English)
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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