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20222026
most citedCan Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?

5 citations · 5 across the 14 of their papers we have counts for

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cs.CV2026

Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models

Chang Ma, Junlin Han, Shuo Chen +3

Red-teaming Text-to-Image (T2I) models is essential for safe deployment, yet it remains particularly challenging against implicit adversarial prompts. Unlike explicit adversarial p…

cs.CV2025

True Multimodal In-Context Learning Needs Attention to the Visual Context

Shuo Chen, Jianzhe Liu, Zhen Han +5

Multimodal Large Language Models (MLLMs), built on powerful language backbones, have enabled Multimodal In-Context Learning (MICL)-adapting to new tasks from a few multimodal demon…

cs.CV2025

METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding

Mengyue Wang, Shuo Chen, Kristian Kersting +2

Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. Nonetheless, processing long videos remains challengin…

cs.CV2024

Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image

Zefeng Wang, Zhen Han, Shuo Chen +6

Multimodal LLMs (MLLMs) with a great ability of text and image understanding have received great attention. To achieve better reasoning with MLLMs, Chain-of-Thought (CoT) reasoning…

cs.CV2023★ 5 cited

Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?

Shuo Chen, Zhen Han, Bailan He +6

Large Language Models (LLMs) with in-context learning (ICL) ability can quickly adapt to a specific context given a few demonstrations (demos). Recently, Multimodal Large Language…