# Zero-Shot Action Recognition in Videos: A Survey

arXiv: https://arxiv.org/abs/1909.06423 · HTML: https://mlsanity.com/paper/1909.06423/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks](https://mlsanity.com/paper/2203.13371/) (2022)
- [Object Priors for Classifying and Localizing Unseen Actions](https://mlsanity.com/paper/2104.04715/) (2021)
- [Cross-modal Representation Learning for Zero-shot Action Recognition](https://mlsanity.com/paper/2205.01657/) (2022)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [Bridging the Skeleton-Text Modality Gap: Diffusion-Powered Modality Alignment for Zero-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2411.10745/) (2024)
- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)
- [Can masking background and object reduce static bias for zero-shot action recognition?](https://mlsanity.com/paper/2501.12681/) (2025)
- [Universal Prototype Transport for Zero-Shot Action Recognition and Localization](https://mlsanity.com/paper/2203.03971/) (2022)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [A Review of Machine Learning Methods Applied to Video Analysis Systems](https://mlsanity.com/paper/2312.05352/) (2023)
- [Aligning Actions and Walking to LLM-Generated Textual Descriptions](https://mlsanity.com/paper/2404.12192/) (2024)
- [Global Semantic Descriptors for Zero-Shot Action Recognition](https://mlsanity.com/paper/2209.12061/) (2022)
- [Region-aware Image-based Human Action Retrieval with Transformers](https://mlsanity.com/paper/2407.09924/) (2024)
- [ACE: Action Concept Enhancement of Video-Language Models in Procedural Videos](https://mlsanity.com/paper/2411.15628/) (2024)
- [How do Foundation Models Compare to Skeleton-Based Approaches for Gesture Recognition in Human-Robot Interaction?](https://mlsanity.com/paper/2506.20795/) (2025)
- [CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition](https://mlsanity.com/paper/2605.01165/) (2026)

## Abstract (arXiv)

Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos. There is a demand for methods that can classify instances from classes that are not present in the training of models, especially in the complex problem of automatic video understanding, since collecting, annotating and labeling videos are difficult and laborious tasks. We have identified that there are many methods available in the literature, however, it is difficult to categorize which techniques can be considered state of the art. Despite the existence of some surveys about zero-shot action recognition in still images and experimental protocol, there is no work focused on videos. Therefore, we present a survey of the methods that comprise techniques to perform visual feature extraction and semantic feature extraction as well to learn the mapping between these features considering specifically zero-shot action recognition in videos. We also provide a complete description of datasets, experiments and protocols, presenting open issues and directions for future work, essential for the development of the computer vision research field.