Papers › Fine-Grained Object Classification via Self-Supervised Pose Alignment

Fine-Grained Object Classification via Self-Supervised Pose Alignment

30 Mar 2022CVPR 2022 1arXiv:2203.15987archive 2025-07-28

Xuhui Yang, YaoWei Wang, Ke Chen, Yong Xu, Yonghong Tian

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.

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