arxiv_cv 93% Match Research Paper Computer Vision Researchers,Machine Learning Engineers,AI Researchers,Robotics Engineers 3 weeks ago

The Role of Video Generation in Enhancing Data-Limited Action Understanding

generative-ai › diffusion

📄 Abstract

Abstract: Video action understanding tasks in real-world scenarios always suffer data limitations. In this paper, we address the data-limited action understanding problem by bridging data scarcity. We propose a novel method that employs a text-to-video diffusion transformer to generate annotated data for model training. This paradigm enables the generation of realistic annotated data on an infinite scale without human intervention. We proposed the information enhancement strategy and the uncertainty-based label smoothing tailored to generate sample training. Through quantitative and qualitative analysis, we observed that real samples generally contain a richer level of information than generated samples. Based on this observation, the information enhancement strategy is proposed to enhance the informative content of the generated samples from two aspects: the environments and the characters. Furthermore, we observed that some low-quality generated samples might negatively affect model training. To address this, we devised the uncertainty-based label smoothing strategy to increase the smoothing of these samples, thus reducing their impact. We demonstrate the effectiveness of the proposed method on four datasets across five tasks and achieve state-of-the-art performance for zero-shot action recognition.

Key Contributions

This paper proposes using text-to-video diffusion models to generate annotated data for training action understanding models, addressing data scarcity. It introduces an 'information enhancement strategy' and 'uncertainty-based label smoothing' to improve the quality and utility of generated data, demonstrating that generated data can significantly boost performance.

Business Value

Enables the development of more capable video understanding systems even with limited real-world data, accelerating AI deployment in areas like autonomous driving, robotics, and content analysis.

Paper Metadata

Innovation Type

Methodological Improvement

Deployment Feasibility

Feasible, as it leverages existing diffusion model architectures and provides strategies to improve generated data quality. Requires significant computational resources for generation.

Limitations Addressed

Data limitations in real-world video action understanding tasks,Difficulty in collecting and annotating large-scale video datasets,Negative impact of low-quality generated data on model training

Performance Gains

Significant improvements in action understanding performance by leveraging generated data, overcoming limitations of data scarcity.

Technical Tags

Video Action UnderstandingData AugmentationText-to-Video GenerationDiffusion ModelsData ScarcityInformation EnhancementLabel SmoothingDeep Learning

Research Topics

Video UnderstandingGenerative ModelsData AugmentationDeep LearningComputer Vision

Methods & Architectures

Text-to-Video Diffusion TransformerInformation Enhancement StrategyUncertainty-based Label SmoothingData Generation Text-to-Video Diffusion Transformer

Applications & Tasks

Video Analysis Robotics Autonomous Systems Human-Computer Interaction Data Scarcity in Video DatasetsImproving Action Understanding ModelsGenerating Realistic Annotated Video Data Enhancing video action understanding modelsGenerating synthetic training dataImproving robustness to data limitations

Related Fields

Computer VisionDeep LearningGenerative ModelsVideo ProcessingMachine Learning

Keywords

Video Action UnderstandingData AugmentationText-to-Video GenerationDiffusion ModelsData ScarcityGenerative AISynthetic DataInformation EnhancementLabel SmoothingDeep LearningComputer Vision

Academic Context

#Video Understanding#Generative Models#Data Augmentation#Deep Learning#Computer Vision

Commercial Potential

Potential Products

Tools for generating synthetic video datasetsAction understanding models trained with augmented dataPlatforms for data-efficient AI training

Target Industries

Autonomous VehiclesRoboticsSecurity and SurveillanceMedia and EntertainmentGaming

Use Case Examples

Training autonomous driving systems with diverse simulated driving scenariosDeveloping robots that can understand a wider range of human actionsEnhancing video content analysis tools with limited training data

Competitive Edge

Offers a novel approach to data augmentation for video action understanding by leveraging advanced text-to-video generation, specifically addressing data scarcity.

Market Opportunity

Growing market for AI training data solutions and synthetic data generation.

Revenue Models

SaaS for synthetic data generationlicensing of generation models.

Resource Requirements

Compute Needs

High computational requirements for training and running text-to-video diffusion models.

Data Requirements

Requires text prompts and potentially some real video data for fine-tuning or evaluation.

Deployment Constraints

Computational cost of generating video data,Ensuring the quality and relevance of generated data,Potential for domain shift between generated and real data

Scalability

Scalability depends on the efficiency of the diffusion model and the generation process.

Regulatory Considerations

Ethical considerations regarding the generation of synthetic data and potential biases.

Production Readiness

Maturity Level

Research/Development

Time to Market

2-4 years for practical integration into training pipelines.

Patent Potential

Moderate, for the information enhancement strategy and the specific application of diffusion models for data augmentation.

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