Modeling Label Correlations with Latent Context for Multi-Label Recognition

Label dependencies have been widely studied in multi-label image recognition for improving performances. Previous methods mainly considered label co-occurrences as label correlations. In this paper, we show that label co-occurrences may be insufficient to represent label correlations, and modeling label correlations relies on latent context information. To this end, we propose a latent context embedding information network for multi-label image recognition. Our proposal is straightforward and contains three key modules to correspondingly tackle three questions, \ie, where to locate the latent context information, how to utilize the latent context information, and how to model label correlations with context-aware features. First, the multi-level context feature fusion module fuses the multi-level feature pyramids to obtain sufficient latent context information. Second, the latent context information embedding module aggregates the latent context information into categorical features, and thus the label correlation can be directly established. Moreover, we use the label correlation capturing module to model label correlations with full and partial manners, respectively. Comprehensive experiments validate the correctness of our arguments and the effectiveness of our method. In both generic multi-label classification and partial-label multi-label classification, our proposed method consistently achieves promising results.