2 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report
Junjie Zhou, Ke Mei, Lei Li +3
Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retr…
Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings
Peixi Wu, Ke Mei, Feipeng Ma +15
Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reasoning-driven generative mult…
D-ORCA: Dialogue-Centric Optimization for Robust Audio-Visual Captioning
Changli Tang, Tianyi Wang, Fengyun Rao +2
Spoken dialogue is a primary source of information in videos; therefore, accurately identifying who spoke what and when is essential for deep video understanding. We introduce D-OR…
WAVE: Learning Unified & Versatile Audio-Visual Embeddings with Multimodal LLM
Changli Tang, Qinfan Xiao, Ke Mei +3
While embeddings from multimodal large language models (LLMs) excel as general-purpose representations, their application to dynamic modalities like audio and video remains underex…
FlexSelect: Flexible Token Selection for Efficient Long Video Understanding
Yunzhu Zhang, Yu Lu, Tianyi Wang +3
Long-form video understanding poses a significant challenge for video large language models (VideoLLMs) due to prohibitively high computational and memory demands. In this paper, w…
From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment
Yucheng Suo, Fan Ma, Linchao Zhu +3
Multi-modal Large language models (MLLMs) show remarkable ability in video understanding. Nevertheless, understanding long videos remains challenging as the models can only process…
HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization
Zitang Zhou, Ke Mei, Yu Lu +2
This paper introduces HarmonySet, a comprehensive dataset designed to advance video-music understanding. HarmonySet consists of 48,328 diverse video-music pairs, annotated with det…