This paper proposes a novel transformer-based framework that aims to enhance
weakly supervised semantic segmentation (WSSS) by generating accurate
class-specific object localization maps as pseudo labels. Building upon the
observation that the attended regions of the one-class token in the standard
vision transformer can contribute to a class-agnostic localization map, we
explore the potential of the transformer model to capture class-specific
attention for class-discriminative object localization by learning multiple
class tokens. We introduce a Multi-Class Token transformer, which incorporates
multiple class tokens to enable class-aware interactions with the patch tokens.
To achieve this, we devise a class-aware training strategy that establishes a
one-to-one correspondence between the output class tokens and the ground-truth
class labels. Moreover, a Contrastive-Class-Token (CCT) module is proposed to
enhance the learning of discriminative class tokens, enabling the model to
better capture the unique characteristics and properties of each class. As a
result, class-discriminative object localization maps can be effectively
generated by leveraging the class-to-patch attentions associated with different
class tokens. To further refine these localization maps, we propose the
utilization of patch-level pairwise affinity derived from the patch-to-patch
transformer attention. Furthermore, the proposed framework seamlessly
complements the Class Activation Mapping (CAM) method, resulting in
significantly improved WSSS performance on the PASCAL VOC 2012 and MS COCO 2014
datasets. These results underline the importance of the class token for WSSS.

本文提出了一种新颖的基于 Transformer 的框架，旨在通过生成准确的类特定目标定位图作为伪标签来增强弱监督语义分割，并探讨了 Transformer 模型捕捉类特定关注力以实现具有类鉴别性的目标定位的潜力。