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20232026
most citedHow to Configure Good In-Context Sequence for Visual Question Answering

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

LEAD: Layer-wise Expert-aligned Decoding for Faithful Radiology Report Generation

Ruixiao Yang, Yuanhe Tian, Xu Yang +3

Radiology Report Generation aims to produce accurate and coherent diagnostics from medical images. Although large vision-language models improve report fluency and accuracy, they s…

cs.CV2025

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs

Huiyi Chen, Jiawei Peng, Dehai Min +5

Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, exist…

cs.CV2024★ 1 cited

Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models

Yuhang Huang, Zihan Wu, Chongyang Gao +2

Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data. Despite their popularity, the capacity of L…

cs.CV2023★ 3 cited

How to Configure Good In-Context Sequence for Visual Question Answering

Li Li, Jiawei Peng, Huiyi Chen +2

Inspired by the success of Large Language Models in dealing with new tasks via In-Context Learning (ICL) in NLP, researchers have also developed Large Vision-Language Models (LVLMs…

cs.CV2023

Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition

Zihao Lin, Jinrong Li, Fan Yang +4

Offline Handwritten Mathematical Expression Recognition (HMER) has been dramatically advanced recently by employing tree decoders as part of the encoder-decoder method. Despite the…