5 papers
Teaching Vision-Language-Action Models What to See and Where to Look
Yuguang Yang, Canyu Chen, Zhewen Tan +10
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing VLAs' training relies heavily on text-centric visual q…
DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
Chen Shi, Jinrui Xu, Shaoshuai Shi +3
Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primarily on static image-text pairs…
Semore: VLM-guided Enhanced Semantic Motion Representations for Visual Reinforcement Learning
Wentao Wang, Chunyang Liu, Kehua Sheng +2
The growing exploration of Large Language Models (LLM) and Vision-Language Models (VLM) has opened avenues for enhancing the effectiveness of reinforcement learning (RL). However,…
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation
Yuanbin Wang, Shaofei Huang, Yulu Gao +5
Traditional 3D segmentation methods can only recognize a fixed range of classes that appear in the training set, which limits their application in real-world scenarios due to the l…
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Dongcheng Zou, Senzhang Wang, Xuefeng Li +5
Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often struggle t…