A New Split for Evaluating True Zero-Shot Action Recognition

Shreyank N Gowda, Laura Sevilla-Lara, Kiyoon Kim, Frank Keller, Marcus Rohrbach

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 8.9% 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 17.1%. We publish our splits (Splits can be found at https://github.com/kini5gowda/TruZe ) and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.

Original languageEnglish
Title of host publicationProceedings of the 43rd DAGM German Conference on Pattern Recognition (GCPR 2021)
EditorsChristian Bauckhage, Juergen Gall, Alexander Schwing
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Number of pages15
ISBN (Electronic)978-3-030-92659-5
ISBN (Print)978-3-030-92658-8
Publication statusPublished - 13 Jan 2022
Event43rd DAGM German Conference on Pattern Recognition - Virtual
Duration: 28 Sep 20211 Oct 2021

Publication series

ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd DAGM German Conference on Pattern Recognition
Abbreviated titleGCPR 2021
Internet address

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