PIAA improves multi-label recognition by enhancing patch-wise predictions via semantic disentanglement and an unsupervised visual classifier, then adaptively aggregating them, yielding over 6% mAP gain on NUS-WIDE in a fully training-free manner.
Boosting single positive multi-label classification with generalized robust loss
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[CLS] is Not Enough: Multi-Label Recognition via Patch-Level Inference and Adaptive Aggregation
PIAA improves multi-label recognition by enhancing patch-wise predictions via semantic disentanglement and an unsupervised visual classifier, then adaptively aggregating them, yielding over 6% mAP gain on NUS-WIDE in a fully training-free manner.