most citedVideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2025

PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval

Zihan Niu, Zheyong Xie, Shaosheng Cao +9

Social chatbots have become essential intelligent companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive…

cs.IR2024

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval

Suyuan Huang, Chao Zhang, Yuanyuan Wu +12

Dense retrieval in most industries employs dual-tower architectures to retrieve query-relevant documents. Due to online deployment requirements, existing real-world dense retrieval…

cs.CL2024

Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents

Shiwei Wu, Chen Zhang, Yan Gao +4

Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly…

cs.CL2024

Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM

Yuanjie Lyu, Tong Xu, Zihan Niu +3

The prosperity of social media platforms has raised the urgent demand for semantic-rich services, e.g., event and storyline attribution. However, most existing research focuses on…

cs.CV2024★ 2 cited

VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation

Shiwei Wu, Joya Chen, Kevin Qinghong Lin +7

A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also sign…

cs.IR2024★ 1 cited

NoteLLM-2: Multimodal Large Representation Models for Recommendation

Chao Zhang, Haoxin Zhang, Shiwei Wu +6

Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks. However, their potential in multimodal representation, particularl…