most citedEvaluation and Analysis of Hallucination in Large Vision-Language Models

26 citations · 49 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2024

ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency

Shaocheng Yan, Pengcheng Shi, Jiayuan Li

Recent advances in point cloud registration mostly leverage geometric information. Although these methods have yielded promising results, they still struggle with problems of low o…

eess.IV2024

Centerline Boundary Dice Loss for Vascular Segmentation

Pengcheng Shi, Jiesi Hu, Yanwu Yang +3

Vascular segmentation in medical imaging plays a crucial role in analysing morphological and functional assessments. Traditional methods, like the centerline Dice (clDice) loss, en…

cs.CV20231 cited

Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration

Yifan Xie, Jihua Zhu, Shiqi Li +1

The majority of point cloud registration methods currently rely on extracting features from points. However, these methods are limited by their dependence on information obtained f…

cs.CV2023

Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing

Jie Zhang, Pengcheng Shi, Zaiwang Gu +2

The neural rendering of humans is a topic of great research significance. However, previous works mostly focus on achieving photorealistic details, neglecting the exploration of hu…

cs.CV20231 cited

Overlap Bias Matching is Necessary for Point Cloud Registration

Pengcheng Shi, Jie Zhang, Haozhe Cheng +4

Point cloud registration is a fundamental problem in many domains. Practically, the overlap between point clouds to be registered may be relatively small. Most unsupervised methods…

cs.LG202326 cited

Evaluation and Analysis of Hallucination in Large Vision-Language Models

Junyang Wang, Yiyang Zhou, Guohai Xu +9

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many…