153 citations · 213 across the 39 of their papers we have counts for
7 papers · 1 filter
ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression
Zijing Cai, Yuzhe Wang, Jingxian Zhu +2
Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods either compress each input into a s…
MURE: Hierarchical Multi-Resolution Encoding via Vision-Language Models for Visual Document Retrieval
Fengbin Zhu, Zijing Cai, Yuzhe Wang +5
Visual Document Retrieval (VDR) requires representations that capture both fine-grained visual details and global document structure to ensure retrieval efficacy while maintaining…
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition
Yiyan Xu, Wuqiang Zheng, Wenjie Wang +5
Personalized image generation has emerged as a promising direction in multimodal content creation. It aims to synthesize images tailored to individual style preferences (e.g., colo…
Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector
Youcheng Huang, Fengbin Zhu, Jingkun Tang +4
Visual Language Models (VLMs) are vulnerable to adversarial attacks, especially those from adversarial images, which is however under-explored in literature. To facilitate research…
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Fengbin Zhu, Ziyang Liu, Xiang Yao Ng +6
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insuffic…
VideoQA in the Era of LLMs: An Empirical Study
Junbin Xiao, Nanxin Huang, Hangyu Qin +8
Video Large Language Models (Video-LLMs) are flourishing and has advanced many video-language tasks. As a golden testbed, Video Question Answering (VideoQA) plays pivotal role in V…
Towards Complex Document Understanding By Discrete Reasoning
Fengbin Zhu, Wenqiang Lei, Fuli Feng +3
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural La…