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.