Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work, we argue that the knowledge bias between instances and contexts affects the capability of the prototype to sufficiently understand instance semantics. Inspired by prototype learning theory, we propose leveraging prototype awareness to capture diverse and fine-grained feature attributes of instances. The hypothesis is that contextual prototypes might erroneously activate similar and frequently co-occurring object categories due to this knowledge bias. Therefore, we propose to enhance the prototype representation ability by mitigating the bias to better capture spatial coverage in semantic object regions. With this goal, we present a Context Prototype-Aware Learning (CPAL) strategy, which leverages semantic context to enrich instance comprehension. The core of this method is to accurately capture intra-class variations in object features through context-aware prototypes, facilitating the adaptation to the semantic attributes of various instances. We design feature distribution alignment to optimize prototype awareness, aligning instance feature distributions with dense features. In addition, a unified training framework is proposed to combine label-guided classification supervision and prototypes-guided self-supervision. Experimental results on PASCAL VOC 2012 and MS COCO 2014 show that CPAL significantly improves off-the-shelf methods and achieves state-of-the-art performance. The project is available at https://github.com/Barrett-python/CPAL.

翻译：近期弱监督语义分割方法致力于结合上下文知识以提升类激活图的完整性。本文指出，实例与上下文之间的知识偏差会影响原型充分理解实例语义的能力。受原型学习理论启发，我们提出利用原型感知来捕获实例多样且细粒度的特征属性。核心假设在于，上下文原型可能因知识偏差而错误激活相似且频繁共现的目标类别。为此，我们提出通过缓解偏差来增强原型表征能力，从而更准确地捕捉语义目标区域的空间覆盖范围。基于该目标，我们提出上下文原型感知学习策略，利用语义上下文丰富实例理解。该方法的核心是通过上下文感知原型精确捕获目标特征的类内变异，进而适应不同实例的语义属性。我们设计特征分布对齐机制以优化原型感知，将实例特征分布与密集特征对齐。此外，提出统一训练框架融合标签引导的分类监督与原型引导的自监督。在PASCAL VOC 2012和MS COCO 2014上的实验表明，本文方法显著提升现有方法性能，达到最先进水平。项目代码见https://github.com/Barrett-python/CPAL。