  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.
