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Conventional multi-label classification (MLC) methods assume that all samplesare fully labeled and identically distributed. Unfortunately, this assumptionis unrealistic in large-scale MLC data that has long-tailed (LT) distributionand partial labels (PL). To address the problem, we introduce a novel task,Partial labeling and Long-Tailed Multi-Label Classification (PLT-MLC), tojointly consider the above two imperfect learning environments. Notsurprisingly, we find that most LT-MLC and PL-MLC approaches fail to solve thePLT-MLC, resulting in significant performance degradation on the two proposedPLT-MLC benchmarks. Therefore, we propose an end-to-end learning framework:\textbf{CO}rrection $\rightarrow$ \textbf{M}odificat\textbf{I}on $\rightarrow$balan\textbf{C}e, abbreviated as \textbf{\method{}}. Our bootstrappingphilosophy is to simultaneously correct the missing labels (Correction) withconvinced prediction confidence over a class-aware threshold and to learn fromthese recall labels during training. We next propose a novel multi-focalmodifier loss that simultaneously addresses head-tail imbalance andpositive-negative imbalance to adaptively modify the attention to differentsamples (Modification) under the LT class distribution. In addition, we developa balanced training strategy by distilling the model's learning effect fromhead and tail samples, and thus design a balanced classifier (Balance)conditioned on the head and tail learning effect to maintain stable performancefor all samples. Our experimental study shows that the proposed \method{}significantly outperforms general MLC, LT-MLC and PL-MLC methods in terms ofeffectiveness and robustness on our newly created PLT-MLC datasets.

Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels