GAPNet

Abstract

This paper proposes a Generic-Attribute-Pose Network (GAPNet) that attends spatial regions to discriminate for fine-grained visual categorization. Compared to a prototypical image classification task with reasonably large variation between classes, fine-grained visual categorization is a task that involves small inter-class variation and large intra-class variation. The GAPNet attends salient regions that can discriminate between classes, but is common within a class. The GAPNet is composed of four streams: the Generic-, Pose-, Part-, and Attribute-stream. The Generic-stream is the main-stream that attends the backbone features with respect to the pose and part-attributes by an attention module referred to as Multi-Attribute Attention Module (MAAM). The Pose-stream extracts pose-specific feature from the backbone feature, while the Part-Attribute streams output features specific pertaining to parts classified in the Pose-stream. The MAAM takes pose, part, and attribute features as query and backbone feature as key-value, and performs cross-inner dot-product between the channels of them followed by max-pooling, to attend the channels of key-value according to best matched query channel. To evaluate the performance of GAPNet, quantitative evaluation and ablation studies on Caltech-UCSD Birds (CUB-Birds) and NABirds are conducted. The functionality of the MAAM module is also verified. Moreover, the effects of each stream in GAPNet are evaluated quantitatively and qualitatively. The influence of stream-order applied to the backbone feature is analyzed. The experiments for GAPNet based on weakly-supervised methods without part annotations are implemented.