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Intra-class variations in the open world lead to various challenges inclassification tasks. To overcome these challenges, fine-grained classificationwas introduced, and many approaches were proposed. Some rely on locating andusing distinguishable local parts within images to achieve invariance toviewpoint changes, intra-class differences, and local part deformations. Ourapproach, which is inspired by P2P-Net, offers an end-to-end trainableattention-based parts alignment module, where we replace the graph-matchingcomponent used in it with a self-attention mechanism. The attention module isable to learn the optimal arrangement of parts while attending to each other,before contributing to the global loss.

Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification