Camouflaged Object Segmentation with Distraction Mining

By Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan. Computer Vision and Pattern Recognition, 2021

507 citations recorded by the source corpus.

Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9e38badadca0cc25da0833c1f3e27fae115486a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Deng-Ping Fan
2. Ming-Ming Cheng
3. Ling Shao
4. Ali Borji
5. Huchuan Lu
6. Jianbing Shen
7. Yuchao Dai
8. Nian Liu

Citations in the corpus

• SAM-Adapter: Adapting Segment Anything in Underperformed Scenes (2023, background): “Recent advances in deep learning-based methods have shown a superior ability to detect complex camouflaged objects [12, 37, 31].”
• Structure-Measure: A New Way to Evaluate Foreground Maps (2017, background): “, 2019)] community has begun to widely adopt this measure even in the camouflaged object detection (Fan et al., 2021a; Zhai et al., 2021; Mei et al., 2021) and medical image segmentation (Fan et al.”
• Context-aware Cross-level Fusion Network for Camouflaged Object Detection (2021, background): “In recent years, camouflage has attracted increasing research interest from the computer vision community [Le et al., 2019; Fan et al., 2021a; Lyu et al., 2021; Mei et al., 2021; Zhai et al., 2021].”
• Simultaneously Localize, Segment and Rank the Camouflaged Objects (2021, methodology): “Existing camouflaged object detection models [10, 22, 61, 33, 25] are designed based on binary ground truth camouflaged object datasets [22, 10, 44] as shown in Fig.”
• Uncertainty-aware Joint Salient Object and Camouflaged Object Detection (2021, methodology): “In this way, the SOD models [48, 46, 42, 38] are designed based on both global contrast and local contrast, while the COD models [11, 26, 35, 52, 34] usually avoid searching the camouflaged objects in those salient regions.”