# A New Split for Evaluating True Zero-Shot Action Recognition

arXiv: https://arxiv.org/abs/2107.13029 · HTML: https://mlsanity.com/paper/2107.13029/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Latent Embeddings for Zero-shot Classification](https://mlsanity.com/paper/1603.08895/) (2016)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Temporal-Relational CrossTransformers for Few-Shot Action Recognition](https://mlsanity.com/paper/2101.06184/) (2021)
- [Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications](https://mlsanity.com/paper/2003.01455/) (2020)
- [Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition](https://mlsanity.com/paper/1904.08703/) (2019)
- [Open Set Domain Adaptation for Image and Action Recognition](https://mlsanity.com/paper/1907.12865/) (2019)
- [Zero-Shot Visual Recognition via Bidirectional Latent Embedding](https://mlsanity.com/paper/1607.02104/) (2016)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)

## Built on by (most cited first, 14 of 14)

- [Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language](https://mlsanity.com/paper/2203.03598/) (2022)
- [Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition](https://mlsanity.com/paper/2206.04790/) (2022)
- [FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks](https://mlsanity.com/paper/2203.13371/) (2022)
- [Synthetic Sample Selection for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2304.02846/) (2023)
- [Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning](https://mlsanity.com/paper/2407.08130/) (2024)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)
- [Continual Learning Improves Zero-Shot Action Recognition](https://mlsanity.com/paper/2410.10497/) (2024)
- [Telling Stories for Common Sense Zero-Shot Action Recognition](https://mlsanity.com/paper/2309.17327/) (2023)
- [Global Semantic Descriptors for Zero-Shot Action Recognition](https://mlsanity.com/paper/2209.12061/) (2022)
- [Is Temporal Prompting All We Need For Limited Labeled Action Recognition?](https://mlsanity.com/paper/2504.01890/) (2025)
- [End-to-End Semantic Video Transformer for Zero-Shot Action Recognition](https://mlsanity.com/paper/2203.05156/) (2022)
- [Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts](https://mlsanity.com/paper/2401.11406/) (2024)
- [Punching Bag vs. Punching Person: Motion Transferability in Videos](https://mlsanity.com/paper/2508.00085/) (2025)
- [Novel Semantic Prompting for Zero-Shot Action Recognition](https://mlsanity.com/paper/2603.08289/) (2026)

## Abstract (arXiv)

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 TruZesplits 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 oursplits1and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.