Unsupervised Attribute Learning

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  1. learn attribute vector based on the relation and difference between different categories (each dimension if uninterpretable): [1] (Laplacian matrix), [2] (triplet loss)

  2. exploit local information and encode them into attribute vector (each dimension is interpretable): [3] (discriminative cluster, doublets), [4] (joint attribute learning and feature learning)

  3. learn attention map for each latent attribute [5]

Reference

  1. Yu, Felix X., et al. “Designing category-level attributes for discriminative visual recognition.” CVPR, 2013.

  2. Li, Yan, et al. “Discriminative learning of latent features for zero-shot recognition.” CVPR, 2018.

  3. Singh, Saurabh, Abhinav Gupta, and Alexei A. Efros. “Unsupervised discovery of mid-level discriminative patches.” ECCV, 2012.

  4. Huang, Chen, Chen Change Loy, and Xiaoou Tang. “Unsupervised learning of discriminative attributes and visual representations.” CVPR, 2016.

  5. Yang, Wenjie, et al. “Towards rich feature discovery with class activation maps augmentation for person re-identification.” CVPR, 2019.