Cross-View Representation Learning for Multi-View Logo Classification with Information Bottleneck

Multi-view logo classification is a challenging task due to the cross-view misalignment of logo image varies under different viewpoints, large intra-classes and small inter-classes variation of logo appearance. Cross-view data can represent objects from different views and thus provide complementary information for data analysis. However, most existing multi-view algorithms usually maximize the correlation between different views for consistency. Those methods ignore the interaction among different views and may cause semantic bias during the process of common feature learning. In this paper, we investigate the information bottleneck (IB) to the multi-view learning for extracting the different view common features of one category, named Dual-View Information Bottleneck representation (Dual-view IB). To the best of our knowledge, this is the first cross-view learning method for logo classification. Specifically, we maximize the mutual information between the representations of the two views to achieve the preservation of key features in the classification task, while eliminating the redundant information that is not shared between the two views. In addition, due to the unbalance of samples and limited computing resources, we further introduce a novel Pair Batch Data Augmentation (PB) algorithm for Dual-view IB model, which applies augmentations from a learned policy based on replicates instances of two samples within the same batch. Comprehensive experiments on three existing benchmark datasets, which demonstrate the effectiveness of the proposed method that outperforms the methods in the state of the art. The proposed method is expected to further the development of cross-view representation learning.