Multi-Label Image Recognition with Joint Class-Aware Map Disentangling and Label Correlation Embedding

Multi-label image recognition is a fundamental but challenging computer vision task. Great progress has been achieved by exploring the label correlation among these multiple labels which is the most crucial issue for multi-label recognition. In this paper, we propose a unified deep learning framework to jointly disentangle class-specific maps corresponding to discriminative category-wise information and then evaluate the label co-occurrence of these maps. Specifically, after obtaining the general deep image features and conducting multi-label classification, we employ the classification weights to reform the feature maps into class-aware disentangled maps (CADMs). Then, based on CADMs, we first transfer them into label vectors and then formulate the label correlation dependency from an embedding perspective. The whole model is driven by both the classification loss and the label correlation embedding loss, which is end-to-end trainable with only image-level supervisions. Extensive quantitative results of two benchmark multi-label image datasets show our model consistently outperforms other competing methods by a large margin. Meanwhile, qualitative analyses also demonstrate our model can effectively capture relatively pure class-aware maps and model label correlation dependency as well.