  Although various methods have been proposed for multi-label classification,
most approaches still follow the feature learning mechanism of the single-label
(multi-class) classification, namely, learning a shared image feature to
classify multiple labels. However, we find this
One-shared-Feature-for-Multiple-Labels (OFML) mechanism is not conducive to
learning discriminative label features and makes the model non-robustness. For
the first time, we mathematically prove that the inferiority of the OFML
mechanism is that the optimal learned image feature cannot maintain high
similarities with multiple classifiers simultaneously in the context of
minimizing cross-entropy loss. To address the limitations of the OFML
mechanism, we introduce the One-specific-Feature-for-One-Label (OFOL) mechanism
and propose a novel disentangled label feature learning (DLFL) framework to
learn a disentangled representation for each label. The specificity of the
framework lies in a feature disentangle module, which contains learnable
semantic queries and a Semantic Spatial Cross-Attention (SSCA) module.
Specifically, learnable semantic queries maintain semantic consistency between
different images of the same label. The SSCA module localizes the label-related
spatial regions and aggregates located region features into the corresponding
label feature to achieve feature disentanglement. We achieve state-of-the-art
performance on eight datasets of three tasks, \ie, multi-label classification,
pedestrian attribute recognition, and continual multi-label learning.
