1 citations · 2 across the 4 of their papers we have counts for
5 papers
COVLM-RL: Critical Object-Oriented Reasoning for Autonomous Driving Using VLM-Guided Reinforcement Learning
Lin Li, Yuxin Cai, Jianwu Fang +2
End-to-end autonomous driving frameworks face persistent challenges in generalization, training efficiency, and interpretability. While recent methods leverage Vision-Language Mode…
Generating Dialogues from Egocentric Instructional Videos for Task Assistance: Dataset, Method and Benchmark
Lavisha Aggarwal, Vikas Bahirwani, Lin Li +1
Many everyday tasks ranging from fixing appliances, cooking recipes to car maintenance require expert knowledge, especially when tasks are complex and multi-step. Despite growing i…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks
Saptarashmi Bandyopadhyay, Vikas Bahirwani, Lavisha Aggarwal +3
Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn dev…
Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
Yaqi Zhao, Yuanyang Yin, Lin Li +7
Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components, often using CLIP-ViT as vision backbone. Howe…