arXiv:2509.09151v2 Announce Type: replace-cross Abstract: Research in video understanding has advanced rapidly, driven by increasingly diverse datasets and more powerful model architectures. While existing surveys typically organize progress by tasks, benchmarks, or model families, they provide limited insight into why particular architectures emerged and succeeded. In this survey, we argue that the evolution of video understanding is fundamentally shaped by dataset structure. We present a dataset-centric perspective that connects dataset structure, inductive biases, and architectural design w
This survey emerges as video understanding research advances rapidly, highlighting the critical role of dataset design in shaping model architectures and capabilities.
Understanding the fundamental link between dataset structure and AI model evolution is crucial for strategic investment and development in AI, informing how future systems will be built and what their limitations might be.
The focus in video understanding shifts from merely improving models to critically evaluating and designing datasets, emphasizing data quality and structure as foundational to architectural success.
- · AI data engineers
- · Dataset curators
- · Deep learning researchers
- · AI platform providers
- · Companies relying on generic datasets
- · AI model developers ignoring data design
- · Inferior video understanding models
Improved video understanding models emerge due to better dataset design and understanding of inductive biases.
New AI applications leveraging advanced video understanding become commercially viable across various sectors.
The role of data curation and engineering becomes paramount, potentially leading to a specialized industry focused on 'dataset-as-a-service' for AI model development.
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Read at arXiv cs.LG