2 citations · 4 across the 8 of their papers we have counts for
11 papers
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…
Anchored Policy Optimization: Mitigating Exploration Collapse Via Support-Constrained Rectification
Tianyi Wang, Long Li, Hongcan Guo +5
Reinforcement Learning with Verifiable Rewards (RLVR) is increasingly viewed as a tree pruning mechanism. However, we identify a systemic pathology termed Recursive Space Contracti…
Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction
Junjie An, Jingguang Tian, Tianyi Wang +3
End-to-end automatic speech recognition (ASR) systems frequently misrecognize domain-specific phrases like named entities, which can cause catastrophic failures in downstream tasks…
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…
LLM Sensitivity Evaluation Framework for Clinical Diagnosis
Chenwei Yan, Xiangling Fu, Yuxuan Xiong +4
Large language models (LLMs) have demonstrated impressive performance across various domains. However, for clinical diagnosis, higher expectations are required for LLM's reliabilit…
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…
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Jianwei Wang, Tianyin Wang, Ziqian Zeng
The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot class…