42 citations · 70 across the 9 of their papers we have counts for
10 papers
MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval
Xu Yuan, Hua Liu, Wenqi Fan +1
Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evidence matching but incur substa…
AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization
Xu Yuan, Yi Wang, Zhuohang Jiang +8
Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable intelligence. Smart glasses incr…
FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering
Yujuan Ding, Linyin Luo, Shijie Wang +5
Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transforme…
SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
Zhuohang Jiang, Xu Yuan, Haohao Qu +4
The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Visual Question Answering (VQA) ov…
mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Xu Yuan, Liangbo Ning, Qingqing Ye +2
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external…
QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering
Zhuohang Jiang, Pangjing Wu, Xu Yuan +2
Retrieval-Augmented Generation (RAG) has been introduced to mitigate hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge into the generat…
HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning
Zhuohang Jiang, Pangjing Wu, Ziran Liang +7
Structure reasoning is a fundamental capability of large language models (LLMs), enabling them to reason about structured commonsense and answer multi-hop questions. However, exist…
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
Zheng Zhang, Xu Yuan, Lei Zhu +2
Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited generalization and inferior stealt…
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model
Li Zhou, Xu Yuan, Zenghui Sun +2
Large Multimodal Models (LMMs) have achieved significant progress by extending large language models. Building on this progress, the latest developments in LMMs demonstrate the abi…
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Xu Yuan, Zheng Zhang, Xunguang Wang +1
Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized th…