5 citations · 8 across the 9 of their papers we have counts for
9 papers
CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning
Ming Li, Chenguang Wang, Yijun Liang +6
Recent agentic Multi-Modal Large Language Models (MLLMs) such as GPT-o3 have achieved near-ceiling scores on various existing benchmarks, motivating a demand for more challenging t…
FALCON: Future-Aware Learning with Contextual Object-Centric Pretraining for UAV Action Recognition
Ruiqi Xian, Xiyang Wu, Tianrui Guan +3
We introduce FALCON, a unified self-supervised video pretraining approach for UAV action recognition from raw RGB aerial footage, requiring no additional preprocessing at inference…
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
Xiyang Wu, Tianrui Guan, Dianqi Li +9
Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect r…
AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales
Tianrui Guan, Ruiqi Xian, Xijun Wang +4
We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps. AGL-NET tackles two critical challenges: bridging the represe…
On the Vulnerability of LLM/VLM-Controlled Robotics
Xiyang Wu, Souradip Chakraborty, Ruiqi Xian +6
In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While…
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Tianrui Guan, Fuxiao Liu, Xiyang Wu +9
We introduce HallusionBench, a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visu…
LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments
Chak Lam Shek, Xiyang Wu, Wesley A. Suttle +5
Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context fro…
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Xiyang Wu, Rohan Chandra, Tianrui Guan +2
Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intenti…
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams
Esmaeil Seraj, Xiyang Wu, Matthew Gombolay
The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic…