← 研踪 PaperTrailCVF · CVPR 2026:cvf:2386c8bb68165e73b5777d08d720c194 · 记录 #1
PriVi: Towards a General-Purpose Video Model for Primate Behavior in the Wild
Mueller, Felix B.Meier, Jan F.Lueddecke, TimoVogg, RichardFreixanet, Roger L.Hassler, ValentinBosshard, TiffanyKarakoc, ElifO'Hearn, William J.Pereira, Sofia M.Sehner, SandroWierucka, KajaBurkart, JudithFichtel, ClaudiaFischer, JuliaGail, AlexanderHobaiter, CatherineOstner, JuliaSamuni, LiranSchülke, OliverShahidi, NedaWessling, Erin G.Ecker, Alexander S. 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:03
摘要
Non-human primates are our closest living relatives, and analyzing their behavior is central to research in cognition, evolution, and conservation. Computer vision could greatly aid this research, but existing methods often rely on human-centric pretrained models and focus on single datasets, which limits generalization. We address this limitation by shifting from a model-centric to a data-centric approach and introduce PriVi, a large-scale primate-centric video pretraining dataset. PriVi contains 424 hours of curated video, combining 174 hours from behavioral research across 11 settings with 250 hours of diverse web-sourced footage, assembled through a scalable data curation pipeline. We continue pretraining V-JEPA, a large-scale video model, on PriVi to learn primate-specific representations and evaluate it using a lightweight frozen classifier. Across four benchmark datasets - ChimpACT, PanAf500, BaboonLand, and ChimpBehave - our approach consistently outperforms prior work, including fully finetuned baselines, and scales favorably with fewer labels. These results demonstrate for the first time that domain-level pretraining, where pretraining is conducted on similar data but not the target dataset itself, works for video models. Our primate-centric pretraining substantially improves data efficiency and generalization, making it a promising approach for low-label applications. Dataset, code, and models are available at https://privi.eckerlab.org.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
PriVi: Towards a General-Purpose Video Model for Primate Behavior in the Wild
Non-human primates are our closest living relatives, and analyzing their behavior is central to research in cognition, evolution, and conservation. Computer vision could greatly aid this research, but existing methods often rely on human-centric pretrained models and focus on single datasets, which limits generalization. We address this limitation by shifting from a model-centric to a data-centric approach and introduce PriVi, a large-scale primate-centric video pretraining dataset. PriVi contains 424 hours of curated video, combining 174 hours from behavioral research across 11 settings with 250 hours of diverse web-sourced footage, assembled through a scalable data curation pipeline. We continue pretraining V-JEPA, a large-scale video model, on PriVi to learn primate-specific representations and evaluate it using a lightweight frozen classifier. Across four benchmark datasets - ChimpACT, PanAf500, BaboonLand, and ChimpBehave - our approach consistently outperforms prior work, including fully finetuned baselines, and scales favorably with fewer labels. These results demonstrate for the first time that domain-level pretraining, where pretraining is conducted on similar data but not the target dataset itself, works for video models. Our primate-centric pretraining substantially improves data efficiency and generalization, making it a promising approach for low-label applications. Dataset, code, and models are available at https://privi.eckerlab.org.
查看此版本解读 →查看此版本原文 ↗