FALCON：面向持续语义场景理解的对比注意力公平性学习方法
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
AI总结：
针对持续语义场景分割中的公平性缺失、未知类别特征无区分度等问题，提出FALCON方法，通过公平性对比聚类损失和注意力视觉语法，在多个基准上取得最优性能并提升模型公平性。
摘要：
语义场景分割中的持续学习旨在动态环境中持续学习新的未见过的类别，同时保留先前学到的知识。已有研究聚焦于建模持续学习中的灾难性遗忘和背景偏移挑战。然而，公平性这一另一重大挑战仍未得到妥善解决，它会导致预测不公平，造成主次类别间性能差异。此外，现有方法尚未能很好地建模未知类别，导致未知类别间产生无区分度的特征。本研究提出一种名为FALCON的新型持续语义场景理解公平性学习方法，即基于对比注意力的公平性学习方法。具体而言，我们首先提出一种全新的公平性对比聚类损失，以解决灾难性遗忘和公平性问题。随后，我们提出一种基于注意力的视觉语法方法，有效建模背景偏移问题与未知类别，为不同未知类别生成更优的特征表示。实验结果表明，我们提出的方法在ADE20K、Cityscapes和Pascal VOC等多个持续学习基准上取得了当前最优（SoTA）性能，提升了持续语义分割模型的公平性。
英文摘要：
Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic forgetting and background shift challenges in continual learning. However, fairness, another major challenge that causes unfair predictions leading to low performance among major and minor classes, still needs to be well addressed. In addition, prior methods have yet to model the unknown classes well, thus resulting in producing non-discriminative features among unknown classes. This work presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes, producing better feature representations for different unknown classes. Through our experiments, our proposed approach achieves State-of-the-Art (SoTA) performance on different continual learning benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC. It promotes the fairness of the continual semantic segmentation model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
