Referring Camouflaged Object Detection With Multi-Context Overlapped Windows Cross-Attention
This work addresses the problem of detecting hidden objects with reference information for computer vision applications, representing an incremental improvement over prior methods.
The paper tackles referring camouflaged object detection by proposing RFMNet, which fuses multi-context features from reference images with camouflage features using an overlapped windows cross-attention mechanism, achieving state-of-the-art performance on the Ref-COD benchmark.
Referring camouflaged object detection (Ref-COD) aims to identify hidden objects by incorporating reference information such as images and text descriptions. Previous research has transformed reference images with salient objects into one-dimensional prompts, yielding significant results. We explore ways to enhance performance through multi-context fusion of rich salient image features and camouflaged object features. Therefore, we propose RFMNet, which utilizes features from multiple encoding stages of the reference salient images and performs interactive fusion with the camouflage features at the corresponding encoding stages. Given that the features in salient object images contain abundant object-related detail information, performing feature fusion within local areas is more beneficial for detecting camouflaged objects. Therefore, we propose an Overlapped Windows Cross-attention mechanism to enable the model to focus more attention on the local information matching based on reference features. Besides, we propose the Referring Feature Aggregation (RFA) module to decode and segment the camouflaged objects progressively. Extensive experiments on the Ref-COD benchmark demonstrate that our method achieves state-of-the-art performance.