arxiv_cv 93% Match Research Paper Computer Vision Researchers,Robotics Engineers,AI Researchers,Human-Computer Interaction Specialists 3 weeks ago

ProbRes: Probabilistic Jump Diffusion for Open-World Egocentric Activity Recognition

computer-vision › video-understanding

📄 Abstract

Abstract: Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided exploration with likelihood-driven exploitation. Our approach integrates structured commonsense priors to construct a semantically coherent search space, adaptively refines predictions using Vision-Language Models (VLMs) and employs a stochastic search mechanism to locate high-likelihood activity labels while minimizing exhaustive enumeration efficiently. We systematically evaluate ProbRes across multiple openness levels (L0-L3), demonstrating its adaptability to increasing search space complexity. In addition to achieving state-of-the-art performance on benchmark datasets (GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego), we establish a clear taxonomy for open-world recognition, delineating the challenges and methodological advancements necessary for egocentric activity understanding. Our results highlight the importance of structured search strategies, paving the way for scalable and efficient open-world activity recognition.

Key Contributions

ProbRes introduces a novel probabilistic jump-diffusion search framework for open-world egocentric activity recognition. It effectively balances exploration and exploitation using commonsense priors and VLM refinement, enabling efficient inference of unseen activities from large, partially observed search spaces and achieving state-of-the-art performance.

Business Value

Enables more intelligent and adaptable AI systems for applications like assistive robotics, personalized user interfaces, and advanced surveillance by understanding human activities in real-world, unconstrained environments.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Feasible for research and advanced applications, but requires significant computational resources and integration with VLM components.

Limitations Addressed

Unconstrained nature of open-world egocentric activity recognition,Difficulty in inferring unseen activities,Inefficiency of exhaustive search in large activity spaces

Performance Gains

Achieves state-of-the-art performance, demonstrating adaptability to increasing search space complexity.

Technical Tags

Egocentric Activity RecognitionOpen-World LearningJump DiffusionProbabilistic SearchCommonsense PriorsVision-Language Models (VLMs)Stochastic SearchActivity Recognition

Research Topics

Video UnderstandingActivity RecognitionOpen-World AIProbabilistic ModelingComputer Vision

Methods & Architectures

Probabilistic Residual SearchJump-Diffusion ProcessCommonsense Prior IntegrationVision-Language Model RefinementStochastic Search Mechanism Probabilistic Search FrameworkVision-Language Models (VLMs)

Applications & Tasks

Human-Computer Interaction Robotics Surveillance Augmented Reality Open-World Activity RecognitionInferring Unseen ActivitiesLarge Search Space NavigationData Scarcity for Novel Activities Recognizing egocentric activitiesAdapting to new activitiesEfficiently searching activity spaces

Datasets & Benchmarks

Datasets

GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, Charades-Ego

Benchmarks

State-of-the-art performance on GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego datasets across multiple openness levels (L0-L3).

AccuracyPerformance across different openness levels

Related Fields

Computer VisionActivity RecognitionMachine LearningProbabilistic MethodsArtificial IntelligenceRobotics

Keywords

Egocentric Activity RecognitionOpen-World LearningJump DiffusionProbabilistic SearchCommonsense ReasoningVision-Language ModelsActivity RecognitionStochastic SearchComputer VisionHuman Activity Understanding

Academic Context

#Video Understanding#Activity Recognition#Open-World AI#Probabilistic Modeling#Computer Vision

Commercial Potential

Potential Products

Intelligent assistants for AR/VR environmentsRobotic systems capable of understanding human actionsAdvanced surveillance systems

Target Industries

RoboticsAugmented RealityHealthcare (assistive technologies)SecurityConsumer Electronics

Use Case Examples

A robot understanding a user's cooking activity to provide assistanceAn AR system recognizing user actions to provide context-aware informationAnalyzing human behavior in surveillance footage for anomaly detection

Competitive Edge

Provides a more robust and efficient solution for open-world egocentric activity recognition compared to methods limited to closed-set assumptions or exhaustive search.

Market Opportunity

Growing market for AI-powered video analysis and activity recognition.

Revenue Models

Licensing of algorithmsdevelopment of specialized AI modules for robotics and AR/VR.

Resource Requirements

Compute Needs

High computational requirements, especially for training VLMs and running the diffusion/search process.

Data Requirements

Requires egocentric video datasets with diverse activities, potentially including annotations for unseen activities.

Deployment Constraints

Real-time processing demands,Integration with VLM components,Need for large-scale training data

Scalability

The framework is designed to handle increasing search space complexity, indicating good scalability for open-world scenarios.

Regulatory Considerations

Privacy concerns related to egocentric video data collection and analysis.

Production Readiness

Maturity Level

Research/Development

Time to Market

3-5 years for practical deployment in complex systems.

Patent Potential

Moderate, for the jump-diffusion search mechanism and its application.

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