Multi-label Recognition of Paintings with Cascaded Attention Network

Yue Li, Tingting Wang, Guangwei Huang, Xiaojun Tang

Abstract: Convolutional neural networks (CNNs) have demonstrated advanced performance on image multi-label classification. However, recognizing labels of paintings is still a challenging problem due to the huge collection and labeling cost on painting training set. Inspired by the similarity between natural image and painting image, we propose an approach based on progressive learning to solve this issue by use of a few labeled paintings. In addition, we set up an effective framework built upon visual cascaded attention for multi-label image classification. Different from the existing approaches, the proposed model extracts and integrates multi-scale features to learn discriminative feature representations, which are then fed to the class-wise attention module with a simple scheme. Experimental results on the challenging benchmark MS-COCO dataset show that our proposed model achieves the best performance compared to the state-of-the-art models. We also demonstrate the effectiveness of the model on our constructed painting testing datasets (Datasets will be made publicly available soon.).