Object segmentation with only image labels using intermediate patch features with ToCo
Object segmentation with only image labels using intermediate patch features with ToCo
Token Contrast for Weakly-Supervised Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2303.01267
arXiv PDF paper https://arxiv.org/pdf/2303.01267.pdf
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels.
... propose Token Contrast (ToCo) to address this issue and further explore the virtue of ViT for WSSS.
Firstly, motivated by the observation that intermediate layers in ViT can still retain semantic diversity ... designed a Patch Token Contrast module (PTC).
PTC supervises the final patch tokens with the pseudo token relations derived from intermediate layers, allowing them to align the semantic regions and thus yield more accurate CAM.
Secondly, to further differentiate the low-confidence regions in CAM ... devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.
... ToCo can remarkably surpass other single-stage competitors and achieve comparable performance with state-of-the-art multi-stage methods ...
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