Zero-shot action recognition is the task of classifying action categories that are not available in the training set. In this setting, the standard evaluation protocol is to use existing action recognition datasets (e.g. UCF101) and randomly split the classes into seen and unseen. However, most recent work builds on representations pre-trained on the Kinetics dataset, where classes largely overlap with classes in the zero-shot evaluation datasets. As a result, classes which are supposed to be unseen, are present during supervised pre-training, invalidating the condition of the zero-shot setting. A similar concern was previously noted several years ago for image based zero-shot recognition, but has not been considered by the zero-shot action recognition community. In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes. We benchmark several recent approaches on the proposed True Zero-Shot (TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation. In our extensive analysis we find that our TruZe splits are significantly harder than comparable random splits as nothing is leaking from pre-training, i.e. unseen performance is consistently lower, up to 9.4% for zero-shot action recognition. In an additional evaluation we also find that similar issues exist in the splits used in few-shot action recognition, here we see differences of up to 14.1%. We publish our splits and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.

该论文针对零样本行为识别中的现有问题，提出了True Zero-Shot(TruZe)数据集，其中训练集、测试集和预训练类别没有任何重叠，对零样本行为识别任务进行了评估，并发现在该任务中未见类别性能普遍较低，实验结果也暗示少样本行为识别问题中存在类似的问题。

一种用于真正零样本动作识别评估的新分割方法