Summary of Pat: Pixel-wise Adaptive Training For Long-tailed Segmentation, by Khoi Do et al.
PAT: Pixel-wise Adaptive Training for Long-tailed Segmentation
by Khoi Do, Duong Nguyen, Nguyen H. Tran, Viet Dung Nguyen
First submitted to arxiv on: 8 Apr 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 The proposed Pixel-wise Adaptive Training (PAT) technique addresses challenges in long-tailed segmentation learning by introducing two key features: class-wise gradient magnitude homogenization and pixel-wise class-specific loss adaptation. This approach alleviates imbalance among label masks and tackles the detrimental impact of rare classes, inaccurate predictions, and forgetting previously learned knowledge. The combined method fosters robust learning while preventing model forgetting. Experimental results show significant performance improvements on three popular datasets, including the NyU dataset, with a notable decline in detecting rare classes compared to Balance Logits Variation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to teach computers to recognize objects in images when there are many more of one type than others. This is called long-tailed segmentation. The authors suggest two special tricks to help the computer learn better: first, make sure it considers all types of objects equally, and second, adjust how it learns based on how confident it is about each object. This combination helps the computer remember what it has learned and doesn’t forget important things. The results show that this approach works well on several image datasets. |
Keywords
» Artificial intelligence » Logits
