A generalized pseudo-label-robust loss plus dynamic CLIP pseudo-labeling achieves state-of-the-art mAP on VOC, COCO, NUS-WIDE, and CUB in the single-positive multi-label setting.
Cdul: Clip-driven unsupervised learning for multi-label image classification
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More Reliable Pseudo-labels, Better Performance: A Generalized Approach to Single Positive Multi-label Learning
A generalized pseudo-label-robust loss plus dynamic CLIP pseudo-labeling achieves state-of-the-art mAP on VOC, COCO, NUS-WIDE, and CUB in the single-positive multi-label setting.