Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly

TL;DR

This paper evaluates the current state of zero-shot learning by establishing a unified benchmark, introducing a new dataset, and analyzing various methods to identify limitations and guide future research.

Contribution

It creates a standardized benchmark and dataset for zero-shot learning, enabling fair comparison and comprehensive analysis of existing methods.

Findings

Established a unified evaluation protocol and data splits.

Introduced the Animals with Attributes 2 (AWA2) dataset.

Provided in-depth analysis of state-of-the-art zero-shot learning methods.

Abstract

Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.