5 papers · 1 filter
Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection
Huafeng Chen, Yueming Lyu, Chenyang Si +3
Camouflaged object detection (COD) aims to segment objects that are visually concealed in their surroundings and has attracted increasing attention in recent years. However, most e…
Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Guangqian Guo, Pengfei Chen, Yong Guo +3
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is ch…
Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion
Chenguang Zhu, Shan Gao, Huafeng Chen +5
Multi-modality image fusion aims to integrate the merits of images from different sources and render high-quality fusion images. However, existing feature extraction and fusion met…
Just a Hint: Point-Supervised Camouflaged Object Detection
Huafeng Chen, Dian Shao, Guangqian Guo +1
Camouflaged Object Detection (COD) demands models to expeditiously and accurately distinguish objects which conceal themselves seamlessly in the environment. Owing to the subtle di…
SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection
Huafeng Chen, Pengxu Wei, Guangqian Guo +1
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches…