May 9, 2024, 4:45 a.m. | Qi Lai, Chi-Man Vong

cs.CV updates on

arXiv:2405.04913v1 Announce Type: new
Abstract: Weakly supervised semantic segmentation (WSSS) aims at learning a semantic segmentation model with only image-level tags. Despite intensive research on deep learning approaches over a decade, there is still a significant performance gap between WSSS and full semantic segmentation. Most current WSSS methods always focus on a limited single image (pixel-wise) information while ignoring the valuable inter-image (semantic-wise) information. From this perspective, a novel end-to-end WSSS framework called DSCNet is developed along with two innovations: …

abstract arxiv current deep learning gap image information performance research segmentation semantic tags type via weakly-supervised

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