2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Hi-Token: Hierarchical Coordinate Tokenization for Generative Visual Grounding
Xiuyuan Zhu, Ke Lu, Kun Dong +6
Generative Vision-Language Models (VLMs) express bounding boxes as coordinate sequences, whose token identities often leave axis and place value implicit. We study how explicitly e…
IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models
Xiuyuan Zhu, Ke Lu, Hao Wu +4
Visual grounding with multimodal large language models is commonly formulated as autoregressive coordinate generation, where a model outputs bounding-box coordinates as text given…
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
Kangan Qian, ChuChu Xie, Yang Zhong +13
Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud…
Smooth Operator: Smooth Verifiable Reward Activates Spatial Reasoning Ability of Vision-Language Model
Siwen Jiao, Tianxiong Lv, Kangan Qian +7
Vision-Language Models (VLMs) face a critical bottleneck in achieving precise numerical prediction for 3D scene understanding. Traditional reinforcement learning (RL) approaches, p…
A Survey on Vision-Language-Action Models for Autonomous Driving
Sicong Jiang, Zilin Huang, Kangan Qian +17
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
Siwen Jiao, Yangyi Fang, Baoyun Peng +2
Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions.…