2 citations · 6 across the 21 of their papers we have counts for
26 papers
FastVLA: Task-Conditioned and Temporally Adaptive Visual Token Compression for Efficient Vision-Language-Action Models
Jiuyi Xu, Chengyuan Qian, Zhengzhong Tu +2
Vision-Language-Action (VLA) models present a promising paradigm for general-purpose robot manipulation, but closed-loop deployment is often limited by inference efficiency: at eac…
Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors
Fan Pu, Keshu Wu, Jiwan Jiang +5
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorr…
CinematicVQA: Benchmarking Film-Grammar Reasoning in Large Vision-Language Models
Shuo Xing, Pooja Verlani, Balu Adsumilli +1
Cinematography, the craft of visual storytelling through framing, lighting, and camera operation, fundamentally shapes how audiences perceive and emotionally engage with video cont…
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
Kexin Tian, Sixu Li, Keshu Wu +2
Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics. Existing meth…
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
Keshu Wu, Chenchen Kuai, Zihao Li +6
Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based…
Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs
Chaoyang Wang, Yangfan He, Yiyang Zhou +6
We reveal a critical yet underexplored flaw in Large Vision-Language Models (LVLMs): even when these models know the correct answer, they frequently arrive there through incorrect…
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Fangzhou Lin, Yuping Wang, Yuliang Guo +7
Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…
KANMixer: a minimal KAN-centered mixer for long-term time series forecasting
Lingyu Jiang, Dengzhe Hou, Yuping Wang +9
Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains chal…
SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems
Peiran Li, Xinkai Zou, Zhuohang Wu +9
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoning and multi-modal tool use. Des…
V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving
Xuewen Luo, Fengze Yang, Fan Ding +5
Autonomous driving (AD) has achieved significant progress, yet single-vehicle perception remains constrained by sensing range and occlusions. Vehicle-to-Everything (V2X) communicat…
VISTA: Generative Visual Imagination for Vision-and-Language Navigation
Yanjia Huang, Mingyang Wu, Renjie Li +1
Vision-and-Language Navigation (VLN) tasks agents with locating specific objects in unseen environments using natural language instructions and visual cues. Many existing VLN appro…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
Chan-Wei Hu, Yueqi Wang, Shuo Xing +4
Large Vision-Language Models (LVLMs) have made remarkable strides in multimodal tasks such as visual question answering, visual grounding, and complex reasoning. However, they rema…
CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Pardis Taghavi, Tian Liu, Renjie Li +2
Instance segmentation demands costly per-pixel annotations and computationally expensive models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that c…
VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction
Zhiwen Fan, Jian Zhang, Renjie Li +15
The rapid advancement of Large Multimodal Models (LMMs) for 2D images and videos has motivated extending these models to understand 3D scenes, aiming for human-like visual-spatial…
LangCoop: Collaborative Driving with Language
Xiangbo Gao, Yuheng Wu, Rujia Wang +3
Multi-agent collaboration holds great promise for enhancing the safety, reliability, and mobility of autonomous driving systems by enabling information sharing among multiple conne…
NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving
Kexin Tian, Jingrui Mao, Yunlong Zhang +3
Recent advancements in Vision-Language Models (VLMs) have demonstrated strong potential for autonomous driving tasks. However, their spatial understanding and reasoning-key capabil…
The Role of Open-Source LLMs in Shaping the Future of GeoAI
Xiao Huang, Zhengzhong Tu, Xinyue Ye +1
Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis, and decision support. This…
Can Large Vision Language Models Read Maps Like a Human?
Shuo Xing, Zezhou Sun, Shuangyu Xie +6
In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenari…
PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing
Yanjia Huang, Renjie Li, Zhengzhong Tu
We present PANDORA, a novel diffusion-based policy learning framework designed specifically for dexterous robotic piano performance. Our approach employs a conditional U-Net archit…
V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors
Keshu Wu, Pei Li, Yang Zhou +8
The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainab…
UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
Yuping Wang, Xiangyu Huang, Xiaokang Sun +4
We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy pr…
Complex LLM Planning via Automated Heuristics Discovery
Hongyi Ling, Shubham Parashar, Sambhav Khurana +6
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on…
Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
Shuo Xing, Peiran Li, Yuping Wang +6
The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby…