4 citations · 5 across the 8 of their papers we have counts for
9 papers
TopoAgent: A Self-Evolving Topological Agent for Multimodal Scientific Reasoning
Mingze Xu, Yinghui Li, Jiayi Kuang +5
While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning. Such…
ANX: Protocol-First Design for AI Agent Interaction with a Supporting 3EX Decoupled Architecture
Xu Mingze
AI agents, autonomous digital actors, need agent-native protocols; existing methods include GUI automation and MCP-based skills, with defects of high token consumption, fragmented…
VideoCoF: Unified Video Editing with Temporal Reasoner
Xiangpeng Yang, Ji Xie, Yiyuan Yang +4
Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temp…
AToken: A Unified Tokenizer for Vision
Jiasen Lu, Liangchen Song, Mingze Xu +5
We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existi…
UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation
Rui Tian, Mingfei Gao, Mingze Xu +5
We introduce UniGen, a unified multimodal large language model (MLLM) capable of image understanding and generation. We study the full training pipeline of UniGen from a data-centr…
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
Haibo Wang, Bo Feng, Zhengfeng Lai +6
We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in ad…
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding
Mingze Xu, Mingfei Gao, Shiyu Li +7
We introduce SlowFast-LLaVA-1.5 (abbreviated as SF-LLaVA-1.5), a family of video large language models (LLMs) offering a token-efficient solution for long-form video understanding.…
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang, Mingfei Gao, Zhe Gan +20
We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Mingze Xu, Mingfei Gao, Zhe Gan +5
We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture detailed spatial semantics and long-range temporal cont…