Summary of A Survey Of Camouflaged Object Detection and Beyond, by Fengyang Xiao et al.
A Survey of Camouflaged Object Detection and Beyond
by Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jinfa Huang, Chunming He, Longxiang Tang, Ziyun Yang, Xiu Li
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel computer vision task, Camouflaged Object Detection (COD), has garnered attention for its potential applications in surveillance, wildlife conservation, and autonomous systems. This comprehensive review explores COD methods across four domains, combining traditional and deep learning approaches. The paper investigates correlations between COD and other camouflaged scenario methods, laying the theoretical foundation for subsequent analyses. Extended methods for instance-level tasks, including segmentation, counting, and ranking, are also summarized. The review provides an overview of commonly used benchmarks and evaluation metrics in COD tasks, evaluating deep learning-based techniques in image and video domains. Finally, the paper discusses limitations of current COD models and proposes nine promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Camouflaged Object Detection (COD) is a new way to help computers find objects that blend into their surroundings. This could be useful for things like surveillance cameras or self-driving cars. Researchers have been working on this problem, but they haven’t written a big overview of what they’ve done yet. So, we’re doing it! We looked at lots of different ways to do COD and found some patterns between them. We also talked about how to measure if the computer is good at finding objects or not. The paper says that right now, computers are pretty bad at this task, but there are lots of ideas for how to make them better. |
Keywords
» Artificial intelligence » Attention » Deep learning » Object detection
