3 citations · 5 across the 14 of their papers we have counts for
14 papers
VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs
Naishan Zheng, Jie Huang, Qingpei Guo +1
Understanding long videos with multimodal large language models (MLLMs) remains challenging due to the heavy redundancy across frames and the need for temporally coherent represent…
OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs
Yuting Gao, Weihao Chen, Lan Wang +2
Preference learning has recently emerged as a pivotal strategy for post-training alignment of Multimodal Large Language Models (MLLMs). However, existing approaches predominantly r…
AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
Yuting Gao, Wang Lan, Hengyuan Zhao +3
Multimodal Mixture-of-Experts (MoE) models offer a promising path toward scalable and efficient large vision-language systems. However, existing approaches rely on rigid routing st…
Thinking Ahead: Foresight Intelligence in MLLMs and World Model
Zhantao Gong, Liaoyuan Fan, Qing Guo +3
In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applications such as autonomous driving, yet lar…
Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer
Ziyuan Huang, DanDan Zheng, Cheng Zou +13
Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in dis…
VaccineRAG: Boosting Multimodal Large Language Models' Immunity to Harmful RAG Samples
Qixin Sun, Ziqin Wang, Hengyuan Zhao +6
Retrieval Augmented Generation enhances the response accuracy of Large Language Models (LLMs) by integrating retrieval and generation modules with external knowledge, demonstrating…
M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning
Inclusion AI, :, Fudong Wang +12
Recent advancements in Multimodal Large Language Models (MLLMs), particularly through Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced their reaso…
CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs
Jiaming Zhang, Rui Hu, Qing Guo +1
Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial atta…
Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
Inclusion AI, Biao Gong, Cheng Zou +14
We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifyi…
Ming-Omni: A Unified Multimodal Model for Perception and Generation
Inclusion AI, Biao Gong, Cheng Zou +55
We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. M…
EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models
Linglin Jing, Yuting Gao, Zhigang Wang +5
Recent advancements have shown that the Mixture of Experts (MoE) approach significantly enhances the capacity of large language models (LLMs) and improves performance on downstream…
LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models
Hengyuan Zhao, Ziqin Wang, Qixin Sun +5
Mixture of Experts (MoE) architectures have recently advanced the scalability and adaptability of large language models (LLMs) for continual multimodal learning. However, efficient…
Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model
Fei Shen, Cong Wang, Junyao Gao +4
Recent advances in conditional diffusion models have shown promise for generating realistic TalkingFace videos, yet challenges persist in achieving consistent head movement, synchr…
Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation
Qin Guo, Tianwei Lin
Recently, diffusion-based methods, like InstructPix2Pix (IP2P), have achieved effective instruction-based image editing, requiring only natural language instructions from the user.…