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In this paper, we study the partial multi-label (PML) image classificationproblem, where each image is annotated with a candidate label set consists ofmultiple relevant labels and other noisy labels. Existing PML methods typicallydesign a disambiguation strategy to filter out noisy labels by utilizing priorknowledge with extra assumptions, which unfortunately is unavailable in manyreal tasks. Furthermore, because the objective function for disambiguation isusually elaborately designed on the whole training set, it can be hardlyoptimized in a deep model with SGD on mini-batches. In this paper, for thefirst time we propose a deep model for PML to enhance the representation anddiscrimination ability. On one hand, we propose a novel curriculum baseddisambiguation strategy to progressively identify ground-truth labels byincorporating the varied difficulties of different classes. On the other hand,a consistency regularization is introduced for model retraining to balancefitting identified easy labels and exploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show theproposed method significantly outperforms the SOTA methods.

A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation