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Song, J., Shen, C., Yang, Y., Liu, Y. and Song, M. (2018) Transductive Unbiased Embedding for Zero-Shot Learning. 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-22 June 2018, 1024-1033.
https://doi.org/10.1109/CVPR.2018.00113

has been cited by the following article:

• TITLE: Explanatory Multi-Scale Adversarial Semantic Embedding Space Learning for Zero-Shot Recognition

  AUTHORS: Huiting Li

  KEYWORDS: Zero-Shot Recognition, Semantic Embedding Space, Adversarial Learning, Explanatory Graph

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.12 No.3, March 22, 2022

  ABSTRACT: The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space. Therefore, semantic embedding space learning plays an important role in zero-shot recognition. Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors. However, the discriminative information included in the user-defined attribute vector is limited. In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space. To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders. We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information. The proposed method is evaluated on the AwA2 and CUB dataset. These results show that our proposed method achieves superior performance.