CVPR 2023 - Feature Shrinkage Pyramid for Camouflaged Object Detection

In this episode we discuss Feature Shrinkage Pyramid for Camouflaged Object Detection by Authors: - Zhou Huang - Hang Dai - Tian-Zhu Xiang - Shuo Wang - Huai-Xin Chen - Jie Qin - Huan Xiong Affiliations: - Zhou Huang: Sichuan Changhong Electric Co., Ltd., China; UESTC, China - Hang Dai: University of Glasgow, UK - Tian-Zhu Xiang: G42, UAE - Shuo Wang: ETH Zurich, Switzerland - Huai-Xin Chen: 2UESTC, China - Jie Qin: 6CCST, NUAA, China - Huan Xiong: MBZUAI, UAE. The paper proposes a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet) to improve camouflaged object detection. Current vision transformers have limitations in locality modeling and feature aggregation, resulting in less effective detection of subtle cues from indistinguishable backgrounds. FSPNet addresses these issues with a non-local token enhancement module and a feature shrinkage decoder with adjacent interaction modules. The proposed model outperforms existing competitors on three challenging datasets, demonstrating its effectiveness in camouflaged object detection.