3 citations · 5 across the 9 of their papers we have counts for
12 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…
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…