Search Results for author:
Found 6 papers, 5 papers with code
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
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 adjusting the number of activated experts based on input difficulty.
Probing Multimodal Large Language Models for Global and Local Semantic Representations
The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images.
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
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.
Ranked #59 on
Visual Question Answering
on MM-Vet
A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models
While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations.
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
In-situ monitoring additive manufacturing process with AI edge computing
Inference time of ViTSR and FCN was optimized to 50. 97 ms and 67. 86 ms on AI edge board after operator fusion and model pruning.
