Summary of Learning Camouflaged Object Detection From Noisy Pseudo Label, by Jin Zhang and Ruiheng Zhang and Yanjiao Shi and Zhe Cao and Nian Liu and Fahad Shahbaz Khan
Learning Camouflaged Object Detection from Noisy Pseudo Label
by Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, Fahad Shahbaz Khan
First submitted to arxiv on: 18 Jul 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 research paper presents a novel approach to Camouflaged Object Detection (COD), which is crucial for identifying objects in images where the background and foreground are unclear. Existing methods rely heavily on large-scale pixel-annotated training sets, but this new method uses boxes as prompts, enabling weakly semi-supervised learning with limited fully labeled images. To overcome noisy pseudo labels generated during early learning, the authors propose a noise correction loss that corrects error risk gradients dominated by noisy pixels in the memorization stage. The result is accurate segmentation of camouflaged objects from noisy labels, outperforming state-of-the-art methods when using only 20% of fully labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists have developed a new way to detect objects that are hiding or blended into their surroundings. They did this by creating boxes around the objects and using those boxes as clues for their computer program to learn from. This approach is much faster than previous methods because it doesn’t require humans to label every single pixel in an image. The researchers also developed a special technique to correct mistakes made during learning, which helps their program make more accurate predictions. Overall, this new method is very good at identifying objects that are hiding or blended into their surroundings, even when there’s only limited information available. |
Keywords
» Artificial intelligence » Object detection » Semi supervised
