From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process

EasyChair Preprint 597

Abstract

Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training examples of seen classes. While classical ZSL considers the recognition performance on unseen classes only, generalized zero-shot learning (GZSL) aims at maximizing performance on both seen and unseen classes. In this paper, we propose a new process for training and evaluation in the GZSL setting; this process addresses the gap in performance between samples from unseen and seen classes by penalizing the latter, and enables to select hyper-parameters well-suited to the GZSL task. It can be applied to any existing ZSL approach and leads to a significant performance boost: the experimental evaluation shows that GZSL performance, averaged over eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from 28.2 to 57.1 on AwA2.

Keyphrases: Generalized Zero-Shot Learning, multimodal classification, zero-shot learning

@booklet{EasyChair:597,
  author    = {Yannick Le Cacheux and Hervé Le Borgne and Michel Crucianu},
  title     = {From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process},
  doi       = {10.29007/c8bc},
  howpublished = {EasyChair Preprint 597},
  year      = {EasyChair, 2018}}