Details

Autor(en) / Beteiligte

• Chen, Geng
• Liu, Si-Jie
• Sun, Yu-Jia
• Ji, Ge-Peng
• Wu, Ya-Feng
• Zhou, Tao

Titel

Camouflaged Object Detection via Context-Aware Cross-Level Fusion

Ist Teil von

• IEEE transactions on circuits and systems for video technology, 2022-10, Vol.32 (10), p.6981-6993

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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 (<inline-formula> <tex-math notation="LaTeX">\text{C}^{2}\text{F} </tex-math></inline-formula>-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. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare <inline-formula> <tex-math notation="LaTeX">\text{C}^{2}\text{F} </tex-math></inline-formula>-Net with state-of-the-art (SOTA) models. The results show that <inline-formula> <tex-math notation="LaTeX">\text{C}^{2}\text{F} </tex-math></inline-formula>-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our <inline-formula> <tex-math notation="LaTeX">\text{C}^{2}\text{F} </tex-math></inline-formula>-Net in COD downstream applications. Our code is publicly available at: https://github.com/Ben57882/C2FNet-TSCVT