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Fine-grained visual categorization (FGVC) is an important but challengingtask due to high intra-class variances and low inter-class variances caused bydeformation, occlusion, illumination, etc. An attention convolutional binaryneural tree architecture is presented to address those problems for weaklysupervised FGVC. Specifically, we incorporate convolutional operations alongedges of the tree structure, and use the routing functions in each node todetermine the root-to-leaf computational paths within the tree. The finaldecision is computed as the summation of the predictions from leaf nodes. Thedeep convolutional operations learn to capture the representations of objects,and the tree structure characterizes the coarse-to-fine hierarchical featurelearning process. In addition, we use the attention transformer module toenforce the network to capture discriminative features. The negativelog-likelihood loss is used to train the entire network in an end-to-endfashion by SGD with back-propagation. Several experiments on the CUB-200-2011,Stanford Cars and Aircraft datasets demonstrate that the proposed methodperforms favorably against the state-of-the-arts.

Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization