Camouflaged Object Detection via Context-Aware Cross-Level Fusion

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network ( $\text{C}^{2}\text{F}$ -Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients.…

• NN
  National Natural Science Foundation of China

  Award: 62172228
• SJ
  Shanghai Jiao Tong University

  Award: Scip202102
• FR
  Fundamental Research Funds for the Central Universities

  Award: D5000220213