This study introduces an efficacious approach, Masked Collaborative Contrast
(MCC), to emphasize semantic regions in weakly supervised semantic
segmentation. MCC adroitly incorporates concepts from masked image modeling and
contrastive learning to devise Transformer blocks that induce keys to contract
towards semantically pertinent regions. Unlike prevalent techniques that
directly eradicate patch regions in the input image when generating masks, we
scrutinize the neighborhood relations of patch tokens by exploring masks
considering keys on the affinity matrix. Moreover, we generate positive and
negative samples in contrastive learning by utilizing the masked local output
and contrasting it with the global output. Elaborate experiments on commonly
employed datasets evidences that the proposed MCC mechanism effectively aligns
global and local perspectives within the image, attaining impressive
performance.

本文介绍了一种有效的方法 Masked Collaborative Contrast（MCC），以突出弱监督语义分割中的语义区域。MCC 巧妙地将掩蔽图像建模和对比学习的概念结合起来，设计了可诱导关键字收缩到语义相关区域的 Transformer 块。实验表明，MCC 机制有效地对齐了图像的全局和局部视角，取得了令人印象深刻的性能。