Context-aware Cross-level Fusion Network for Camouflaged Object Detection

By Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu. International Joint Conference on Artificial Intelligence, 2021

330 citations recorded by the source corpus.

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/c8403e50500eaba9fb459eaacca8714ef6e16f60

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Citations in the corpus

Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection (2022, methodology): “70.70.80.80.90.90.91.0 achieved good performance on COD and SOD tasks, the inference speed is still slightly slower than the current fastest method, C2FNet [43].”