Video Understanding by Design: How Datasets Shape Architectures and Insights

2025-09-11

AI Summary

Existing video understanding research overlooks how structural characteristics of datasets—such as motion complexity, temporal span, hierarchical composition, and multimodal richness—guide the evolution of model architectures. Method: We propose a dataset-centric analytical framework that systematically interprets mainstream architectures—including two-stream networks, 3D CNNs, RNNs, Transformers, and multimodal foundation models—as responses to dataset-imposed inductive biases. Our approach integrates literature review with architecture–bias–task alignment analysis, unifying inductive bias theory and multimodal learning paradigms. Contribution/Results: We establish, for the first time, a unified “dataset → inductive bias → model design” framework, revealing the intrinsic logic underlying architectural evolution. The framework yields principled, generalizable design guidelines for video understanding models that balance scalability and task adaptability, advancing both theoretical understanding and practical model development.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Video understanding has advanced rapidly, fueled by increasingly complex datasets and powerful architectures. Yet existing surveys largely classify models by task or family, overlooking the structural pressures through which datasets guide architectural evolution. This survey is the first to adopt a dataset-driven perspective, showing how motion complexity, temporal span, hierarchical composition, and multimodal richness impose inductive biases that models should encode. We reinterpret milestones, from two-stream and 3D CNNs to sequential, transformer, and multimodal foundation models, as concrete responses to these dataset-driven pressures. Building on this synthesis, we offer practical guidance for aligning model design with dataset invariances while balancing scalability and task demands. By unifying datasets, inductive biases, and architectures into a coherent framework, this survey provides both a comprehensive retrospective and a prescriptive roadmap for advancing general-purpose video understanding.

Problem

• Analyzing how video datasets impose structural biases on model architectures
• Reinterpreting model evolution as responses to dataset-driven inductive pressures
• Providing guidance for aligning designs with dataset invariances and scalability

Innovation

• Dataset-driven perspective for model design
• Aligning architectures with dataset invariances
• Unifying datasets, biases, and architectures framework