This work is a systematical analysis on the so-called hard class problem in
zero-shot learning (ZSL), that is, some unseen classes disproportionally affect
the ZSL performances than others, as well as how to remedy the problem by
detecting and exploiting hard classes. At first, we report our empirical
finding that the hard class problem is a ubiquitous phenomenon and persists
regardless of used specific methods in ZSL. Then, we find that high semantic
affinity among unseen classes is a plausible underlying cause of hardness and
design two metrics to detect hard classes. Finally, two frameworks are proposed
to remedy the problem by detecting and exploiting hard classes, one under
inductive setting, the other under transductive setting. The proposed
frameworks could accommodate most existing ZSL methods to further significantly
boost their performances with little efforts. Extensive experiments on three
popular benchmarks demonstrate the benefits by identifying and exploiting the
hard classes in ZSL.

本文的研究重点在于解决零样本学习中的 “难分类” 问题，通过检测和利用难分类，可以显著提高性能。作者经过实证研究得出了难分类是无法避免的问题，并且设计了两个度量来检测难分类，最后提出了两个算法框架，在归纳和横向归纳两种情况下检测和利用难分类，通过实验数据证实，可以显著提高零样本学习的性能。