Fine-Grained Object Classification via Self-Supervised Pose Alignment
This work improves classification accuracy for fine-grained objects like birds or cars, where subtle differences matter, but it is incremental as it builds on existing part-based methods.
The paper tackled fine-grained object classification by addressing pose variations that obscure local part details, proposing a self-supervised pose alignment method that achieved state-of-the-art performance on three benchmarks.
Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, distinctive details carried by local regions can be spatially distributed or even self-occluded, leading to a large variation on object representation. For discounting pose variations, this paper proposes to learn a novel graph based object representation to reveal a global configuration of local parts for self-supervised pose alignment across classes, which is employed as an auxiliary feature regularization on a deep representation learning network.Moreover, a coarse-to-fine supervision together with the proposed pose-insensitive constraint on shallow-to-deep sub-networks encourages discriminative features in a curriculum learning manner. We evaluate our method on three popular fine-grained object classification benchmarks, consistently achieving the state-of-the-art performance. Source codes are available at https://github.com/yangxh11/P2P-Net.