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Found 3 papers, 1 papers with code
Exploiting Low-level Representations for Ultra-Fast Road Segmentation
The spatial detail branch is firstly designed to extract low-level feature representation for the road by the first stage of ResNet-18.
Fully Self-Supervised Learning for Semantic Segmentation
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #13 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation
The graph network that is established with patches as the nodes can maximize the mutual learning of similar objects.
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