Name
Title | ||
|---|---|---|
Camouflaged object detection via Neighbor Connection and Hierarchical Information Transfer |
Abstract | ||
|---|---|---|
Camouflaged Object Detection (COD) aims to detect objects with high similarity to the background. Unlike general object detection, COD is a more challenging task because the target boundaries are vague and the location is difficult to determine. In this paper, we propose a novel COD framework, which consists of two main components, namely, Neighbor Connection Mode (NCM) and Hierarchical Information Transfer (HIT). NCM aggregates the features from the neighboring layers of the encoder network to enhance the complementation of various level information. Our NCM not only reduces the burden of dense connection that consumes a lot of computing memory and redundant features but also weakens the phenomenon of the long-term transmission of context. We also propose a HIT module to transfer the features of different dilated rates inside each level hierarchically, which expands the receptive field of each branch and enhances the relationship between different features. Our method accurately detects camouflaged objects by considering full level information and a large receptive field. The experiments on three COD datasets show that our model achieves state-of-the-art performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cviu.2022.103450 | Computer Vision and Image Understanding |
Keywords | DocType | Volume |
Deep learning,Camouflaged Object Detection,Salient Object Detection | Journal | 221 |
Issue | ISSN | Citations |
1 | 1077-3142 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |