Fully Convolutional Attention Networks for Fine Grained Recognition
Fine grained recognition is challenging due to its subtle local inter class differences versus large intra class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose invariant features. However, ground truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine grained classes. This work introduces Fully Convolutional Attention Networks (FCANs) a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine grained benchmark datasets, including CUB 200 2011, Stanford Dogs, Stanford Cars and Food 101.