6 papers
Making Sense of Touch from the Child's View for Contrastive Learning
Max Whitton, Zecheng Wang, Puchen Liu +12
Is the sense of touch a mechanism for human babies' learning of visual concepts? If so, can we quantify its importance, and to what extent do babies rely on their sense of touch fo…
BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models
Shengao Wang, Wenqi Wang, Zecheng Wang +20
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…
SITE: towards Spatial Intelligence Thorough Evaluation
Wenqi Wang, Reuben Tan, Pengyue Zhu +6
Spatial intelligence (SI) represents a cognitive ability encompassing the visualization, manipulation, and reasoning about spatial relationships, underpinning disciplines from neur…
Fine-grained Token Allocation Via Operation Pruning for Efficient MLLMs
Aoming Liu, Reuben Tan, Boqing Gong +1
Token reduction accelerates Multimodal Large Language Models (MLLMs) by reducing excessive tokens, but overlooks structural redundancy differences, where critical and redundant mod…
BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning
Shengao Wang, Arjun Chandra, Aoming Liu +2
Human infants rapidly develop visual reasoning skills from minimal input, suggesting that developmentally inspired pretraining could significantly enhance the efficiency of vision-…
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging
Aoming Liu, Kevin Miller, Venkatesh Saligrama +4
Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. Prior work often addresses this by predicting future mo…