Search Results for author: Chunhui Li

Found 12 papers, 4 papers with code

AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability

no code implementations23 May 2024 Fei Zhao, Taotian Pang, Chunhui Li, Zhen Wu, Junjie Guo, Shangyu Xing, Xinyu Dai

In the pre-training stage, instead of treating all image-text pairs equally, we assign different levels of alignment capabilities to different image-text pairs.

Language Modelling Large Language Model

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models

no code implementations15 Feb 2024 Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, WeiHao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai

Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination.


CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

1 code implementation27 Oct 2023 Nan Ying, Yanli Lei, Tianyi Zhang, Shangqing Lyu, Chunhui Li, Sicheng Chen, Zeyu Liu, Yu Zhao, Guanglei Zhang

This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.

Self-Supervised Learning Transfer Learning +1

DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking

1 code implementation9 Oct 2023 Shangyu Xing, Fei Zhao, Zhen Wu, Chunhui Li, Jianbing Zhang, Xinyu Dai

Multimodal Entity Linking (MEL) is a task that aims to link ambiguous mentions within multimodal contexts to referential entities in a multimodal knowledge base.

Entity Linking Relation

Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level

no code implementations13 Jun 2023 Mujahid Ali Quidwai, Chunhui Li, Parijat Dube

The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism.


Deep-Learning-based Vasculature Extraction for Single-Scan Optical Coherence Tomography Angiography

no code implementations17 Apr 2023 Jinpeng Liao, Tianyu Zhang, Yilong Zhang, Chunhui Li, Zhihong Huang

In comparison to OCTA images obtained via the SV-OCTA (PSNR: 17. 809) and ED-OCTA (PSNR: 18. 049) using four-repeated OCT scans, OCTA images extracted by VET exhibit moderate quality (PSNR: 17. 515) and higher image contrast while reducing the required data acquisition time from ~8 s to ~2 s. Based on visual observations, the proposed VET outperforms SV and ED algorithms when using neck and face OCTA data in areas that are challenging to scan.

EPCS: Endpoint-based Part-aware Curve Skeleton Extraction for Low-quality Point Clouds

no code implementations17 Nov 2022 Chunhui Li, Mingquan Zhou, Zehua Liu, Yuhe Zhang

In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed.

An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

no code implementations7 Aug 2022 Akram Shafie, Chunhui Li, Nan Yang, Xiangyun Zhou, Trung Q. Duong

Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.

Machine Learning in Heterogeneous Porous Materials

no code implementations4 Feb 2022 Martha D'Eli, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, Geoerge Karniadakid, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre Tartakovsky, Daniel M. Tartakovsky, Hamdi Tchelepi, Bozo Vazic, Hari Viswanathan, Hongkyu Yoon, Piotr Zarzycki

The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.

BIG-bench Machine Learning

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