8 citations · 12 across the 13 of their papers we have counts for
7 papers · 1 filter
SafeNexus: Discovering and Steering Modality-Universal Safety Neurons in MLLMs
Jian Yu, Fei Shen, Cong Wang +5
Although Large Language Models (LLMs) have demonstrated promising safety performance, extending them to Multimodal Large Language Models (MLLMs) exposes a significant gap between e…
One Anchor for All: Unified Multilingual and Multimodal Safety Alignment for LVLMs
Enyi Shi, Fei Shen, Chuancheng Shi +4
As large vision-language models (LVLMs) are deployed globally, the combination of multilingual instructions and visual information makes malicious attacks more covert and sophistic…
MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits
Yixin Xiang, Yunshan Ma, Xiaoyu Du +3
Document Question Answering (DQA) involves generating answers from a document based on a user's query, representing a key task in document understanding. This task requires interpr…
VersaVogue: Visual Expert Orchestration and Preference Alignment for Unified Fashion Synthesis
Jian Yu, Fei Shen, Cong Wang +4
Diffusion models have driven remarkable advancements in fashion image generation, yet prior works usually treat garment generation and virtual dressing as separate problems, limiti…
Targeted Interpretable Safety Neuron Enhancement for Multilingual Vision-Language Large Models
Enyi Shi, Fei Shen, Chuancheng Shi +5
With the widespread deployment of vision-language large models (VLLMs), their safety alignment faces dual challenges across languages and modalities. Existing methods model multili…
IMAGAgent: Orchestrating Multi-Turn Image Editing via Constraint-Aware Planning and Reflection
Fei Shen, Chengyu Xie, Lihong Wang +5
Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and closed-loop feedback mechanisms, they are p…
DMAP: Human-Aligned Structural Document Map for Multimodal Document Understanding
ShunLiang Fu, Yanxin Zhang, Yixin Xiang +2
Existing multimodal document question-answering (QA) systems predominantly rely on flat semantic retrieval, representing documents as a set of disconnected text chunks and largely…