63 citations · 98 across the 7 of their papers we have counts for
6 papers
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models
Chenzhuang Du, Yue Zhao, Chonghua Liao +3
This paper investigates how to better leverage large-scale pre-trained uni-modal models to further enhance discriminative multi-modal learning. Even when fine-tuned with only uni-m…
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
Tianyu Yu, Jinyi Hu, Yuan Yao +10
Recent Multimodal Large Language Models (MLLMs) exhibit impressive abilities to perceive images and follow open-ended instructions. The capabilities of MLLMs depend on two crucial…
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
Construction of unbiased dental template and parametric dental model for precision digital dentistry
Lei Ma, Jingyang Zhang, Ke Deng +8
Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate param…
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
Yue Zhao, Lingming Zhang, Yang Liu +6
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods…
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation
Lingming Zhang, Yue Zhao, Deyu Meng +5
The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods ha…