BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914431109169152 |
|---|---|
| author | Wang, Shengao Wang, Wenqi Wang, Zecheng Whitton, Max Wakeham, Michael Chandra, Arjun Huang, Joey Zhu, Pengyue Chen, Helen Li, David Li, Jeffrey Li, Shawn Zagula, Andrew Zhao, Amy Zhu, Andrew Nakamura, Sayaka Yamamoto, Yuki Yokono, Jerry Jun Mueller, Aaron Plummer, Bryan A. Saenko, Kate Saligrama, Venkatesh Gong, Boqing |
| author_facet | Wang, Shengao Wang, Wenqi Wang, Zecheng Whitton, Max Wakeham, Michael Chandra, Arjun Huang, Joey Zhu, Pengyue Chen, Helen Li, David Li, Jeffrey Li, Shawn Zagula, Andrew Zhao, Amy Zhu, Andrew Nakamura, Sayaka Yamamoto, Yuki Yokono, Jerry Jun Mueller, Aaron Plummer, Bryan A. Saenko, Kate Saligrama, Venkatesh Gong, Boqing |
| contents | 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 the recently released NIH Baby Toolbox into a benchmark suite of ten multimodal tasks, covering spatial reasoning, memory, and vocabulary understanding aligned with early children's capabilities. Experimental results show that a compact model pretrained from scratch can achieve competitive performance on DevCV Toolbox, outperforming GPT-4o on some tasks. We hope the principled, unified BabyVLM-V2 framework will accelerate research in developmentally plausible pretraining of vision foundation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models Wang, Shengao Wang, Wenqi Wang, Zecheng Whitton, Max Wakeham, Michael Chandra, Arjun Huang, Joey Zhu, Pengyue Chen, Helen Li, David Li, Jeffrey Li, Shawn Zagula, Andrew Zhao, Amy Zhu, Andrew Nakamura, Sayaka Yamamoto, Yuki Yokono, Jerry Jun Mueller, Aaron Plummer, Bryan A. Saenko, Kate Saligrama, Venkatesh Gong, Boqing Computer Vision and Pattern Recognition Artificial Intelligence 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 the recently released NIH Baby Toolbox into a benchmark suite of ten multimodal tasks, covering spatial reasoning, memory, and vocabulary understanding aligned with early children's capabilities. Experimental results show that a compact model pretrained from scratch can achieve competitive performance on DevCV Toolbox, outperforming GPT-4o on some tasks. We hope the principled, unified BabyVLM-V2 framework will accelerate research in developmentally plausible pretraining of vision foundation models. |
| title | BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10932 |