Summary of Aucseg: Auc-oriented Pixel-level Long-tail Semantic Segmentation, by Boyu Han et al.
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
by Boyu Han, Qianqian Xu, Zhiyong Yang, Shilong Bao, Peisong Wen, Yangbangyan Jiang, Qingming Huang
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel approach to optimize the Area Under the ROC Curve (AUC) for pixel-level long-tail semantic segmentation tasks. This problem is more challenging than instance-level learning due to complex coupling between loss terms and high memory demands. The authors develop a pixel-level AUC loss function, conduct theoretical analysis using dependency graphs, and design a Tail-Classes Memory Bank (T-Memory Bank) to manage memory complexity. Experimental results on various benchmarks demonstrate the effectiveness of their proposed AUCSeg method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve model performance for long-tail semantic segmentation tasks. It’s like trying to teach a computer to recognize specific objects in an image, but with lots of different types of objects and only a few examples each. The authors come up with a new way to calculate how well the computer is doing, called AUCSeg, which helps it learn better from these kinds of images. |
Keywords
» Artificial intelligence » Auc » Loss function » Roc curve » Semantic segmentation
