Summary of Just a Hint: Point-supervised Camouflaged Object Detection, by Huafeng Chen et al.
Just a Hint: Point-Supervised Camouflaged Object Detection
by Huafeng Chen, Dian Shao, 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 A novel approach to Camouflaged Object Detection (COD) is proposed, tackling the challenge of accurately distinguishing objects that seamlessly blend into their environments. To alleviate the heavy annotation burden, a single point supervision method is introduced, which first expands the original point-based annotation to a hint area and then regulates model attention through partially masking labeled regions. Additionally, unsupervised contrastive learning based on differently augmented image pairs is employed to improve feature representation of camouflaged objects. Experimental results on three mainstream COD benchmarks demonstrate that this approach outperforms several weakly-supervised methods across various metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Camouflaged Object Detection is a tricky task where models need to find objects that are really good at hiding. This makes it hard for humans to label the objects, too. To make it easier, researchers came up with a new way to teach models using just one point of information about each object. They tested this method on several benchmarks and found that it works much better than other methods. |
Keywords
* Artificial intelligence * Attention * Object detection * Supervised * Unsupervised
