Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection
It addresses the challenging problem of detecting camouflaged objects that blend into backgrounds, which is important for applications like surveillance and medical imaging, but appears incremental as it builds on existing transformer and graph-based approaches.
The paper tackles camouflaged object detection by proposing HGINet, a hierarchical graph interaction network with dynamic token clustering, which achieves superior performance on datasets like COD10K, CAMO, NC4K, and CHAMELEON compared to existing state-of-the-art methods.
Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.