Weakly Supervised Local Attention Network for Fine-Grained Visual Classification

In the fine-grained visual classification task, objects usually share similar geometric structure but present different part distribution and variant local features. Therefore, localizing and extracting discriminative local features play a crucial role in obtaining accurate performance. Existing work that first locates specific several object parts and then extracts further local features either require additional location annotation or needs to train multiple independent networks. In this paper. We propose Weakly Supervised Local Attention Network (WS-LAN) to solve the problem, which jointly generates a great many attention maps (region-of-interest maps) to indicate the location of object parts and extract sequential local features by Local Attention Pooling (LAP). Besides, we adopt attention center loss and attention dropout so that each attention map will focus on a unique object part. WS-LAN can be trained end-to-end and achieves the state-of-the-art performance on multiple fine-grained classification datasets, including CUB-200-2011, Stanford Car and FGVC-Aircraft, which demonstrated its effectiveness.