We also observe that no single backbone performs best and that there is still a noticeable performance gap on certain generalization scenarios, motivating investigation into truly versatile backbones and better adaptation strategies.
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We systematically evaluate and compare the performance of leading Video Foundation Models (ViFMs) across a wide range of scientific domains within a unified framework. Our analysis shows that ViFMs provide a strong signal in highly diverse spatiotemporal applications.
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🧑‍💻 Inspect SciVid benchmark data and models with our colab demo: bit.ly/44BuJcS
Curious to evaluate your own video model on SciVid? Our code bit.ly/3Ge92YB provides data download instructions and supports evaluation of a HF 🤗 VideoMAE-B backbone, and is extensible to evaluate your own model.
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Thrilled to share our latest work on SciVid, to appear at #ICCV2025! 🎉
SciVid offers cross-domain evaluation of video models in scientific applications, including medical CV, animal behavior, & weather forecasting 🧪🌍📽️🪰🐭🫀🌦️
📝 Check out our paper: arxiv.org/abs/2507.03578
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