[2512.10932] BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.10932 (cs) [Submitted on 11 Dec 2025 (v1), last revised 29 Mar 2026 (this version, v2)] Title:BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models Authors:Shengao Wang, Wenqi Wang, Zecheng Wang, Max Whitton, Michael Wakeham, Arjun Chandra, Joey Huang, Pengyue Zhu, Helen Chen, David Li, Jeffrey Li, Shawn Li, Andrew Zagula, Amy Zhao, Andrew Zhu, Sayaka Nakamura, Yuki Yamamoto, Jerry Jun Yokono, Aaron Mueller, Bryan A. Plummer, Kate Saenko, Venkatesh Saligrama, Boqing Gong View a PDF of the paper titled BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models, by Shengao Wang and 22 other authors View PDF HTML (experimental) Abstract:Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded framework for infant-inspired vision-language modeling that extensively improves upon BabyVLM-V1 through a longitudinal, multifaceted pretraining set, a versatile model, and, most importantly, DevCV Toolbox for cognitive evaluation. The pretraining set maximizes coverage while minimizing curation of a longitudinal, infant-centric audiovisual corpus, yielding video-utterance, image-utterance, and multi-turn conversational data that mirror infant experiences. DevCV Toolbox adapts all vision-related measures of th...