20 citations · 35 across the 14 of their papers we have counts for
14 papers
VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models
Ruiqi Xian, Yuehan Xian, Jing Liang +2
Semantic 3D occupancy provides a voxelized world state for autonomous driving and robot decision making, but object and rare-class errors can affect free-space interpretation, coll…
CalibFree: Self-Supervised View Feature Separation for Calibration-Free Multi-Camera Multi-Object Tracking
Ruiqi Xian, Deep Patel, Iain Melvin +3
Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives, particularly when precise ca…
GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion
Ruiqi Xian, Jing Liang, He Yin +2
We present \emph{GaussianSSC}, a two-stage, grid-native and triplane-guided approach to semantic scene completion (SSC) that injects the benefits of Gaussians without replacing the…
Bi-VLM: Pushing Ultra-Low Precision Post-Training Quantization Boundaries in Vision-Language Models
Xijun Wang, Junyun Huang, Rayyan Abdalla +3
We address the critical gap between the computational demands of vision-language models and the possible ultra-low-bit weight precision (bitwidth bits) we can use for highe…
Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
Mohamed Elnoor, Kasun Weerakoon, Gershom Seneviratne +5
We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, u…
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…
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition
Xijun Wang, Ruiqi Xian, Tianrui Guan +2
We present a new learning approach, Soft Conditional Prompt Learning (SCP), which leverages the strengths of prompt learning for aerial video action recognition. Our approach is de…
PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
Ruiqi Xian, Xijun Wang, Divya Kothandaraman +1
We present a new algorithm for selection of informative frames in video action recognition. Our approach is designed for aerial videos captured using a moving camera where human ac…
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning
Xijun Wang, Ruiqi Xian, Tianrui Guan +4
We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-…
MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition
Ruiqi Xian, Xijun Wang, Dinesh Manocha
We present a novel approach for action recognition in UAV videos. Our formulation is designed to handle occlusion and viewpoint changes caused by the movement of a UAV. We use the…