163 citations · 489 across the 48 of their papers we have counts for
6 papers · 1 filter
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels
Tao Pu, Qianru Lao, Hefeng Wu +2
As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other a…
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +3
Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-s…
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Tianshui Chen, Tao Pu, Lingbo Liu +3
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and th…
Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation
Yupei Lin, Sen Zhang, Tianshui Chen +3
Unpaired image-to-image translation aims to find a mapping between the source domain and the target domain. To alleviate the problem of the lack of supervised labels for the source…
Semantic Representation and Dependency Learning for Multi-Label Image Recognition
Tao Pu, Mingzhan Sun, Hefeng Wu +3
Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing…
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +1
Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging…