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Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes

large-language-models › multimodal-llms

📄 Abstract

Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities in zero-shot action recognition by learning to associate video embeddings with class embeddings. However, a significant challenge arises when relying solely on action classes to provide semantic context, particularly due to the presence of multi-semantic words, which can introduce ambiguity in understanding the intended concepts of actions. To address this issue, we propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords. This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation. Additionally, we introduce a spatio-temporal interaction module designed to focus on objects and action units, facilitating alignment between description attributes and video content. In our zero-shot experiments, our model achieves impressive results, attaining accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively, underscoring the model's adaptability and effectiveness across various downstream tasks.

Authors (3)

Yehna Kim

Young-Eun Kim

Seong-Whan Lee

Submitted

October 31, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

This paper proposes an innovative approach to enhance spatio-temporal zero-shot action recognition by leveraging language-driven description attributes extracted from web-crawled data using LLMs. This method reduces reliance on manual annotation and introduces a spatio-temporal interaction module to align attributes with video content, improving recognition accuracy.

Business Value

Enables more accurate and flexible video analysis systems that can recognize novel actions without explicit training, valuable for surveillance, content moderation, and robotics.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Moderate. Requires integration of VLM components and efficient processing of video data. LLM integration for attribute extraction adds complexity.

Limitations Addressed

Ambiguity arising from multi-semantic action classes and the need for laborious manual attribute creation in zero-shot action recognition.

Performance Gains

Achieves impressive results in zero-shot experiments, outperforming previous methods by leveraging richer semantic information from descriptions.

Technical Tags

Zero-shot Action RecognitionVision-Language Models (VLMs)Description AttributesLanguage-driven FeaturesSpatio-Temporal ModuleWeb-crawled DescriptionsLarge Language Models (LLMs)Attribute ExtractionAmbiguity Reduction

Research Topics

Computer VisionNatural Language ProcessingMachine LearningAction RecognitionMulti-modal Learning

Methods & Architectures

Language-driven Description AttributesLLM-based Keyword ExtractionSpatio-Temporal Interaction ModuleZero-shot Learning Vision-Language Models (VLMs)Transformer-based Models

Applications & Tasks

Video Analysis Surveillance Robotics Human Activity Recognition Ambiguity in action classesNeed for extensive labeled dataLaborious manual attribute creation Zero-shot Action RecognitionVideo Understanding

Datasets & Benchmarks

Benchmarks

81.0% accuracy • 53.1% accuracy

Accuracy

Related Fields

Computer VisionNatural Language ProcessingMachine LearningRoboticsSurveillance Technology

Keywords

Zero-shot LearningAction RecognitionVision-Language ModelsMulti-modal AIDescription AttributesLLMsSpatio-Temporal AnalysisVideo UnderstandingAttribute ExtractionWeb DataDeep Learning

Academic Context

#Computer Vision#Natural Language Processing#Machine Learning#Action Recognition#Multi-modal Learning

Commercial Potential

Potential Products

Advanced video surveillance systemsAutomated content moderation toolsRobotic systems capable of understanding human actions

Target Industries

SecurityMediaRoboticsRetail

Use Case Examples

Identifying specific actions in security footage without prior training on those actions.Robots understanding and responding to human gestures.

Competitive Edge

Offers a more semantically rich approach to zero-shot action recognition by utilizing LLM-derived attributes, overcoming limitations of class-name-only embeddings and reducing manual annotation effort.

Market Opportunity

Significant market for video analytics and AI-powered understanding.

Revenue Models

Licensing of the recognition modelsintegration into analytics platforms.

Resource Requirements

Compute Needs

Requires substantial GPU resources for training VLMs and processing video data, plus resources for LLM inference.

Data Requirements

Requires video datasets with action labels, and access to web data for description attribute extraction.

Deployment Constraints

Computational cost of VLM and LLM inference,Latency for real-time video analysis

Scalability

Scalability depends on the efficiency of the VLM and the LLM attribute extraction process. Processing large video archives can be resource-intensive.

Regulatory Considerations

Potential privacy concerns if applied to surveillance data.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years for practical deployment.

Patent Potential

Moderate, particularly around the method of using LLM-extracted description attributes for zero-shot recognition.

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