# Text-Enhanced Zero-Shot Action Recognition: A training-free approach

arXiv: https://arxiv.org/abs/2408.16412 · HTML: https://mlsanity.com/paper/2408.16412/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling](https://mlsanity.com/paper/2111.03930/) (2021)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Expanding Language-Image Pretrained Models for General Video Recognition](https://mlsanity.com/paper/2208.02816/) (2022)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [What does a platypus look like? Generating customized prompts for zero-shot image classification](https://mlsanity.com/paper/2209.03320/) (2022)
- [Fine-tuned CLIP Models are Efficient Video Learners](https://mlsanity.com/paper/2212.03640/) (2022)
- [Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://mlsanity.com/paper/2207.03482/) (2022)
- [Waffling around for Performance: Visual Classification with Random Words and Broad Concepts](https://mlsanity.com/paper/2306.07282/) (2023)
- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Verbs in Action: Improving verb understanding in video-language models](https://mlsanity.com/paper/2304.06708/) (2023)
- [Revisiting Classifier: Transferring Vision-Language Models for Video Recognition](https://mlsanity.com/paper/2207.01297/) (2022)
- [UnLoc: A Unified Framework for Video Localization Tasks](https://mlsanity.com/paper/2308.11062/) (2023)
- [Zero-Shot Temporal Action Detection via Vision-Language Prompting](https://mlsanity.com/paper/2207.08184/) (2022)
- [Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition](https://mlsanity.com/paper/1904.08703/) (2019)
- [Temporal Interlacing Network](https://mlsanity.com/paper/2001.06499/) (2020)
- [MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge](https://mlsanity.com/paper/2303.08914/) (2023)
- [Paxion: Patching Action Knowledge in Video-Language Foundation Models](https://mlsanity.com/paper/2305.10683/) (2023)
- [FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2402.03241/) (2024)
- [Test-Time Zero-Shot Temporal Action Localization](https://mlsanity.com/paper/2404.05426/) (2024)
- [Zero-Shot Activity Recognition with Verb Attribute Induction](https://mlsanity.com/paper/1707.09468/) (2017)
- [A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition](https://mlsanity.com/paper/2303.13505/) (2023)
- [Language-based Action Concept Spaces Improve Video Self-Supervised Learning](https://mlsanity.com/paper/2307.10922/) (2023)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)

## Built on by (most cited first, 6 of 6)

- [ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment](https://mlsanity.com/paper/2506.22967/) (2025)
- [Language as a Label: Zero-Shot Multimodal Classification of Everyday Postures under Data Scarcity](https://mlsanity.com/paper/2510.13364/) (2025)
- [Decompose and Transfer: CoT-Prompting Enhanced Alignment for Open-Vocabulary Temporal Action Detection](https://mlsanity.com/paper/2603.24030/) (2026)
- [Single-agent vs. Multi-agents for Automated Video Analysis of On-Screen Collaborative Learning Behaviors](https://mlsanity.com/paper/2604.03631/) (2026)
- [High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions](https://mlsanity.com/paper/2605.00496/) (2026)
- [ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization](https://mlsanity.com/paper/2605.30689/) (2026)

## Abstract (arXiv)

Vision-language models (VLMs) have demonstrated remarkable performance across various visual tasks, leveraging joint learning of visual and textual representations. While these models excel in zero-shot image tasks, their application to zero-shot video action recognition (ZSVAR) remains challenging due to the dynamic and temporal nature of actions. Existing methods for ZS-VAR typically require extensive training on specific datasets, which can be resource-intensive and may introduce domain biases. In this work, we propose Text-Enhanced Action Recognition (TEAR), a simple approach to ZS-VAR that is training-free and does not require the availability of training data or extensive computational resources. Drawing inspiration from recent findings in vision and language literature, we utilize action descriptors for decomposition and contextual information to enhance zero-shot action recognition. Through experiments on UCF101, HMDB51, and Kinetics-600 datasets, we showcase the effectiveness and applicability of our proposed approach in addressing the challenges of ZS-VAR.