CAReFuseNet: Cross Attention Fusion Network for Referring Camouflaged Object Detection

Abstract: The Referring Camouflaged Object Detection (Ref-COD) task aims to generate a binary segmentation mask to detect camouflaged objects of a specified category in an image, guided by reference image(s) containing salient example(s) of the target object of the same category. With only a few methods (e.g., R2CNet and UAT) proposed to date, Ref-COD remains challenging due to the similarity of camouflaged objects to their backgrounds and substantial feature gaps with salient references. At the same time, recent state-of-the-art approaches often rely on heavy transformer-based encoder–decoder stacks or large frozen vision backbones, resulting in substantial parameter footprints that hinder efficient deployment. This work proposes `CAReFuseNet’, a novel framework featuring a cross-attention based reference feature fusion module that effectively extracts reference-conditioned feature representations from camouflaged images while targeting parameter efficiency. The proposed CAReFuse module leverages global interactions between reference and camouflaged image features via cross-attention, but constrains all fusion and decoding operations to a lower dimensional feature space and employs a lightweight convolutional decoder. Combined with a frozen Ref-Image Encoder, this design yields a compact Ref-COD model without sacrificing accuracy. Extensive experiments on the R2C7K dataset show that our method surpasses state-of-the-art, while using significantly fewer parameters. Further evaluations across multiple backbone architectures, including Swin Transformer, ConvNeXt, EfficientNet, and ResNet, demonstrate that the proposed reference feature fusion module provides a general and parameter-efficient building block for the referring camouflaged object detection task.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Yuchao_Dai1

Submission Number: 8125