6 papers
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
Ruihan Xu, Yuting Gao, Lan Wang +5
Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference…
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-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…
Panther: Illuminate the Sight of Multimodal LLMs with Instruction-Guided Visual Prompts
Honglin Li, Yuting Gao, Chenglu Zhu +3
Multimodal large language models (MLLMs) are closing the gap to human visual perception capability rapidly, while, still lag behind on attending to subtle images details or locatin…