About Time: Advances, Challenges, and Outlooks of Action Understanding

2024-11-22

arXiv.org

AI Summary

This survey systematically categorizes video action understanding into three temporal regimes: full-action recognition, partial-observation prediction, and unobserved forecasting—first unifying their distinct modeling essences. Addressing limitations in long-horizon causal reasoning and cross-modal synergy, it comprehensively reviews deep temporal modeling, contrastive learning, multimodal alignment, generative video modeling, and zero-shot transfer, integrating Transformer, CNN-LSTM, and diffusion-based paradigms. Synthesizing over 100 seminal works, it identifies critical performance bottlenecks and evaluation biases, constructing a structured knowledge graph. The core contribution is the proposal of the “dynamic reasoning” paradigm—a conceptual and methodological shift that advances action understanding from static classification toward systematic, causal, anticipatory, and generalizable modeling.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Reasoning under Uncertainty: Causality

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries, synthesize unobserved parts of videos, and predict context across multiple modalities. This survey comprehensively reviews advances in uni- and multi-modal action understanding across a range of tasks. We focus on prevalent challenges, overview widely adopted datasets, and survey seminal works with an emphasis on recent advances. We broadly distinguish between three temporal scopes: (1) recognition tasks of actions observed in full, (2) prediction tasks for ongoing partially observed actions, and (3) forecasting tasks for subsequent unobserved action(s). This division allows us to identify specific action modeling and video representation challenges. Finally, we outline future directions to address current shortcomings.

Problem

• Review advances in uni- and multi-modal action understanding
• Address challenges in action modeling and video representation
• Outline future directions for video action understanding

Innovation

• Advances in uni- and multi-modal action understanding
• Focus on recognition, prediction, and forecasting tasks
• Survey of datasets, challenges, and recent works