Finding the Achilles Heel: Progressive Identification Network for Camouflaged Object Detection
- Resource Type
- Conference
- Authors
- Chou, Mu-Chun; Chen, Hung-Jen; Shuai, Hong-Han
- Source
- 2022 IEEE International Conference on Multimedia and Expo (ICME) Multimedia and Expo (ICME), 2022 IEEE International Conference on. :1-6 Jul, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Measurement
Object detection
Logic gates
Object recognition
Camouflaged object detection
label reweighting
- Language
- ISSN
- 1945-788X
Camouflaged object detection (COD) aims to segment objects assimilating into their surroundings. The key challenge for COD is that there are existing high intrinsic similarities between the target object and the background. To solve this challenging problem, we propose the Cascaded Decamouflage Module to progressively improve the prediction map, where each decamouflage module is composed of the region enhancement block and the reverse attention mining block to accurately detect the camouflaged object and obtain complete target objects. In addition, we introduce the classification-based label reweighting to produce the gated label maps as the supervision for assisting the network to capture the most conspicuous region of a camouflaged object and obtain the target object entirely. Extensive experiments on three challenging datasets demonstrate that the proposed model outperforms state-of-the-art methods under different evaluation metrics.