Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This work addresses the problem of semantic segmentation with weak supervision for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles weakly supervised semantic segmentation by proposing Masked Collaborative Contrast (MCC), which integrates masked image modeling and contrastive learning to highlight semantic regions, achieving impressive performance on standard datasets.
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws inspiration from masked image modeling and contrastive learning to devise a novel framework that induces keys to contract toward semantic 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. The source code is available at \url{https://github.com/fwu11/MCC}.