Action-Dynamics Modeling and Cross-Temporal Interaction for Online Action Understanding

2025-10-12

AI Summary

To address the challenges of abundant redundant noise and insufficient modeling of actor intent in untrimmed videos, this paper proposes a state-specific framework for online action understanding. The method introduces three key innovations: (1) modeling action intent as a critical state transition graph, enabling intent-driven dynamic representation; (2) designing a cross-temporal interaction mechanism that jointly optimizes action detection and future action anticipation; and (3) incorporating critical state memory compression and multi-dimensional edge graph construction to enhance temporal reasoning efficiency and robustness. Evaluated on standard benchmarks—including EPIC-Kitchens-100 and THUMOS’14—the framework achieves significant improvements over state-of-the-art methods, demonstrating superior effectiveness and generalization capability in complex, real-world scenarios.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Action understanding, encompassing action detection and anticipation, plays a crucial role in numerous practical applications. However, untrimmed videos are often characterized by substantial redundant information and noise. Moreover, in modeling action understanding, the influence of the agent's intention on the action is often overlooked. Motivated by these issues, we propose a novel framework called the State-Specific Model (SSM), designed to unify and enhance both action detection and anticipation tasks. In the proposed framework, the Critical State-Based Memory Compression module compresses frame sequences into critical states, reducing information redundancy. The Action Pattern Learning module constructs a state-transition graph with multi-dimensional edges to model action dynamics in complex scenarios, on the basis of which potential future cues can be generated to represent intention. Furthermore, our Cross-Temporal Interaction module models the mutual influence between intentions and past as well as current information through cross-temporal interactions, thereby refining present and future features and ultimately realizing simultaneous action detection and anticipation. Extensive experiments on multiple benchmark datasets -- including EPIC-Kitchens-100, THUMOS'14, TVSeries, and the introduced Parkinson's Disease Mouse Behaviour (PDMB) dataset -- demonstrate the superior performance of our proposed framework compared to other state-of-the-art approaches. These results highlight the importance of action dynamics learning and cross-temporal interactions, laying a foundation for future action understanding research.

Problem

• Reduces video redundancy through critical state compression
• Models action dynamics and intentions using state-transition graphs
• Enables simultaneous detection and anticipation via cross-temporal interactions

Innovation

• Compresses frame sequences into critical states
• Models action dynamics with state-transition graphs
• Uses cross-temporal interactions to refine features