Video Understanding by Design: How Datasets Shape Video Models

A comprehensive survey argues that dataset structure fundamentally shapes the evolution of video understanding models, connecting dataset characteristics to architectural innovations like transformers and multimodal foundation models. The research provides a unified framework explaining how different datasets drive specific inductive biases and architectural choices across video AI development.

This arXiv survey represents a significant shift in how researchers conceptualize progress in video understanding—moving from task-centric or architecture-centric perspectives to a dataset-centric lens. Rather than treating model architectures as isolated innovations, the authors demonstrate that milestone designs including two-stream networks, 3D CNNs, and transformers emerged as direct responses to dataset-specific challenges and requirements. This perspective has substantial implications for AI development strategy. Understanding that datasets drive architectural evolution provides practitioners with clearer guidance on why certain models succeed in particular domains and fail in others. When datasets emphasize temporal ordering sensitivity, models develop temporal attention mechanisms; when datasets require multi-modal reasoning, cross-modal alignment becomes an architectural priority. This framework moves beyond retrospective analysis—it offers predictive power for anticipating which architectural innovations will emerge as new datasets introduce novel challenges like extreme-length temporal reasoning or specialized cross-modal interactions. For developers building video AI systems, the framework suggests that dataset composition decisions are not downstream concerns but primary drivers of model capabilities and generalization patterns. Organizations investing in video understanding should carefully audit their training data structure to ensure it captures the invariances their deployment scenarios require. The research also highlights representational biases inherent to different data regimes, cautioning against assuming models trained on popular benchmarks will transfer universally. As video AI applications expand into specialized domains—from medical imaging to autonomous systems—this dataset-architecture relationship becomes increasingly critical for understanding deployment limitations and guiding future research priorities.

• →Dataset structure fundamentally shapes architectural innovation in video understanding models, not the reverse
• →Different datasets impose distinct requirements for capturing temporal sensitivity, viewpoint robustness, and long-range dependencies
• →Milestone architectures like transformers and multimodal models can be understood as solutions to evolving dataset challenges
• →Dataset-induced representational biases limit generalization across domains despite strong benchmark performance
• →This framework provides both historical explanation and predictive guidance for future video AI development