5 citations · 10 across the 6 of their papers we have counts for
8 papers
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Kai Chen, Ruiyuan Gao, Lanqing Hong +11
In this paper, we present details of the 1st W-CODA workshop, held in conjunction with the ECCV 2024. W-CODA aims to explore next-generation solutions for autonomous driving corner…
PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learning
Xinpeng Ding, Kui Zhang, Jianhua Han +3
Direct Preference Optimization (DPO) helps reduce hallucinations in Video Multimodal Large Language Models (VLLMs), but its reliance on offline preference data limits adaptability…
Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors
Jiawei Shen, Chengcheng Wan, Ruoyi Qiao +6
Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries.…
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
Yunhao Gou, Kai Chen, Zhili Liu +6
Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Althoug…
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
Yunhao Gou, Zhili Liu, Kai Chen +6
Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vi…
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
Jiahui Gao, Renjie Pi, Jipeng Zhang +8
Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mat…
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
Kai Chen, Chunwei Wang, Kuo Yang +11
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when…
Implicit Concept Removal of Diffusion Models
Zhili Liu, Kai Chen, Yifan Zhang +6
Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. These concepts, termed as the "implicit concepts", could b…