AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https: //github. com/boyuh/AUCSeg.

Authors

• Boyu Han
• Qianqian Xu Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
• Zhiyong Yang School of Computer Science and Technology, University of Chinese Academy of Sciences
• Shilong Bao School of Computer Science and Technology, University of Chinese Academy of Sciences
• Peisong Wen School of Computer Science and Tech., University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
• Yangbangyan Jiang School of Computer Science and Technology, University of Chinese Academy of Sciences
• Qingming Huang School of Computer Science and Technology, University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences

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