# Transductive Zero-Shot Action Recognition by Word-Vector Embedding

arXiv: https://arxiv.org/abs/1511.04458 · HTML: https://mlsanity.com/paper/1511.04458/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2015.

## Built on by (most cited first, 30 of 31)

- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization](https://mlsanity.com/paper/2302.00624/) (2023)
- [Zero-shot Natural Language Video Localization](https://mlsanity.com/paper/2110.00428/) (2021)
- [Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models](https://mlsanity.com/paper/2207.07646/) (2022)
- [Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data](https://mlsanity.com/paper/2310.05010/) (2023)
- [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)
- [Synthetic Sample Selection for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2304.02846/) (2023)
- [Towards Zero-shot Sign Language Recognition](https://mlsanity.com/paper/2201.05914/) (2022)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Language-based Action Concept Spaces Improve Video Self-Supervised Learning](https://mlsanity.com/paper/2307.10922/) (2023)
- [C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition](https://mlsanity.com/paper/2407.06113/) (2024)
- [Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment](https://mlsanity.com/paper/2409.14336/) (2024)
- [Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2101.03292/) (2021)
- [Zero-Shot Action Recognition from Diverse Object-Scene Compositions](https://mlsanity.com/paper/2110.13479/) (2021)
- [ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition](https://mlsanity.com/paper/2401.11654/) (2024)
- [Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2303.06817/) (2023)
- [Continual Learning Improves Zero-Shot Action Recognition](https://mlsanity.com/paper/2410.10497/) (2024)
- [Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2202.09545/) (2022)
- [Universal Prototype Transport for Zero-Shot Action Recognition and Localization](https://mlsanity.com/paper/2203.03971/) (2022)
- [Telling Stories for Common Sense Zero-Shot Action Recognition](https://mlsanity.com/paper/2309.17327/) (2023)
- [Zero-Shot Skeleton-Based Action Recognition With Prototype-Guided Feature Alignment](https://mlsanity.com/paper/2507.00566/) (2025)
- [GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition](https://mlsanity.com/paper/2105.11789/) (2021)
- [Generate, Transduct, Adapt: Iterative Transduction with VLMs](https://mlsanity.com/paper/2501.06031/) (2025)
- [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)
- [Learning Using Privileged Information for Zero-Shot Action Recognition](https://mlsanity.com/paper/2206.08632/) (2022)
- [Target inductive methods for zero-shot regression](https://mlsanity.com/paper/2402.01252/) (2024)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)

## Abstract (arXiv)

The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL). To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute-based semantic representations. In this work, we explore word-vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic strategies to improve upon the standard ZSL pipeline. Most of these strategies are transductive in nature which means access to testing data in the training phase.