most citedHarder Tasks Need More Experts: Dynamic Routing in MoE Models

3 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2024

RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance

Zhicheng Sun, Zhenhao Yang, Yang Jin +7

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require tra…

cs.LG20243 cited

Harder Tasks Need More Experts: Dynamic Routing in MoE Models

Quzhe Huang, Zhenwei An, Nan Zhuang +7

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adju…

cs.CL2024

Probing Multimodal Large Language Models for Global and Local Semantic Representations

Mingxu Tao, Quzhe Huang, Kun Xu +3

The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works lever…

cs.CV2024

Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization

Yang Jin, Zhicheng Sun, Kun Xu +9

In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Co…

cs.CL20232 cited

A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

Hejing Cao, Zhenwei An, Jiazhan Feng +3

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with u…