computer-vision-11
Multi-label Image Recognition with Partial Labels
3260 papers • 126 benchmarks • 313 datasets
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each multi-label image, aims to train MLR models with partial labels to reduce the annotation cost. Since existing MLR datasets have complete labels, current works propose to randomly drop a certain proportion of positive and negative labels to create partially annotated datasets, and report the results on the known labels proportion of 10% to 90%.
(Image credit: Papersgraph)
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MS-COCO-2014
MS-COCO-2014
PASCAL VOC 2007
PASCAL VOC 2007
Visual Genome
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Most implemented papers
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Liang Lin, Tianshui Chen, Hefeng Wu, Muxin Xu, X. Hui•Mon Aug 19 2019
A Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism.