3 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis
Li Li, Yongliang Wu, Jingze Zhu +3
The evolution of large models has witnessed the emergence of In-Context Learning (ICL) capabilities. In Natural Language Processing (NLP), numerous studies have demonstrated the ef…
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization
Huiyi Chen, Jiawei Peng, Kaihua Tang +2
In-context learning (ICL) enables Large Vision-Language Models (LVLMs) to adapt to new tasks without parameter updates, using a few demonstrations from a large support set. However…
LIVE: Learnable In-Context Vector for Visual Question Answering
Yingzhe Peng, Chenduo Hao, Xu Yang +3
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefi…
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…
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…