Recently, zero-shot learning (ZSL) has received increasing interest. The key
idea underpinning existing ZSL approaches is to exploit knowledge transfer via
an intermediate-level semantic representation which is assumed to be shared
between the auxiliary and target datasets, and is used to bridge between these
domains for knowledge transfer. The semantic representation used in existing
approaches varies from visual attributes to semantic word vectors and semantic
relatedness. However, the overall pipeline is similar: a projection mapping
low-level features to the semantic representation is learned from the auxiliary
dataset by either classification or regression models and applied directly to
map each instance into the same semantic representation space where a zero-shot
classifier is used to recognise the unseen target class instances with a single
known 'prototype' of each target class. In this paper we discuss two related
lines of work improving the conventional approach: exploiting transductive
learning ZSL, and generalising ZSL to the multi-label case.

本文讨论了两种与传统方法相关的工作：利用传导学习 ZSL 和将 ZSL 推广到多标签情况下。