Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
Although semi-supervised semantic segmentation ($\text{S}^4$) utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data causes the former to dominate, which severely degrades pseudo-label quality. To address this challenges, we propose a novel remote sensing (RS) $\text{S}^4$ method via unified flow with feature memory bank (UFFM). Specifically, UFFM comprises two key innovations: unified flow (UF) and feature memory bank (FMB). The UF is a new training flow that generates less biased pseudo-labels by combining an external visual fou
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- FuzzySimilar name plus overlapping authors · 67%bytedance/deer-flow →
“Title similarity 0.73; shared authors: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Shanwen Wang →
“Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank”
- LinkedLinked via arxiv author · 85%ZhiXin Sun →
“Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank”
- LinkedLinked via arxiv author · 85%Danfeng Hong →
“Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank”
- LinkedLinked via arxiv author · 85%Junyu Dong →
“Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank”
- LinkedLinked via arxiv author · 85%Patrick Le Callet →
“Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank”