5 papers · 1 filter
NICE FACT: Diagnosing and Calibrating VLMs in Quantitative Reasoning for Kinematic Physics
Jian Lan, Zhicheng Liu, Xinpeng Wang +5
The ability to derive precise spatial and physical insights is a cornerstone of vision-language models (VLMs), yet their poor performances in related spatial intelligence tasks suc…
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
Yuhao Zhou, Yunpeng Zhu, Yang Zhou +7
Vision-Language-Action (VLA) models hold great promise for general-purpose robotic intelligence, yet scaling up such models is severely bottlenecked by the high cost of acquiring a…
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
Jian Lan, Zhicheng Liu, Udo Schlegel +5
Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, wh…
AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction
Gengyuan Zhang, Tanveer Hannan, Hermine Kleiner +6
An ideal vision-language agent serves as a bridge between the human users and their surrounding physical world in real-world applications like autonomous driving and embodied agent…
Mind the Uncertainty in Human Disagreement: Evaluating Discrepancies between Model Predictions and Human Responses in VQA
Jian Lan, Diego Frassinelli, Barbara Plank
Large vision-language models frequently struggle to accurately predict responses provided by multiple human annotators, particularly when those responses exhibit human uncertainty.…