Summary of Sam-cod: Sam-guided Unified Framework For Weakly-supervised Camouflaged Object Detection, by Huafeng Chen et al.
SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection
by Huafeng Chen, Pengxu Wei, Guangqian Guo, Shan Gao
First submitted to arxiv on: 20 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 This paper proposes a unified framework for Camouflaged Object Detection (COD), termed SAM-COD, which can handle arbitrary weakly-supervised labels. The framework employs a prompt adapter to utilize scribbles as prompts based on Segment Anything Model (SAM). Additionally, it introduces response filter and semantic matcher modules to improve the quality of masks obtained by SAM under COD prompts. To alleviate inaccurate mask predictions, the paper utilizes prompt-adaptive knowledge distillation to ensure reliable feature representations. Empirical experiments conducted on three mainstream COD benchmarks demonstrate the superiority of the proposed method compared to state-of-the-art weakly-supervised and fully-supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better detect objects that are hidden or hard to see, like animals in a forest. Most computer programs for doing this rely on special annotations, which are difficult to create. This new approach, called SAM-COD, can use different types of labels and is more accurate than previous methods. It even works well when the labels are incomplete or incorrect. The paper tested it on three big datasets and showed that it performed better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Knowledge distillation » Mask » Object detection » Prompt » Sam » Supervised
