AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
<|reference_start|>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.<|reference_end|>
@article{han2024aucseg:,
    title={AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation},
    author={Boyu Han, Qianqian Xu, Zhiyong Yang, Shilong Bao, Peisong Wen,
  Yangbangyan Jiang, Qingming Huang},
    journal={arXiv preprint arXiv:2409.20398},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20398},
    primaryClass={cs.CV cs.AI cs.LG}
}