Name: Y Mao
Type: User
Company: Imperial College London
Bio: I am a first-year PhD at Imperial College London sponsored by the President's Scholarship. My research interest lies in 3D Vision and Multimodal Learning.
Location: London, United Kingdom
Blog: https://yebulabula.github.io/
Y Mao's Projects
A curated list of awesome READMEs
An awesome README template to jumpstart your projects!
Transformer-Based Conditional Adversarial Domain Adaptation in Amplification Curve Analysis
【MICCAI 2023, Early accept】DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
Face_Detection&Recognition System
write ppa coursework
Release for Improved Denoising Diffusion Probabilistic Models
AI-Planning sweet website
【MICCAI 2021, Early accept】Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network
Offical Repository: OpenDlign: Enhancing Open-World 3D Learning with Depth-Aligned Images
official code of “OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding”
Recover sparse signals by orthogonal matching pursuit, iterative hard-thresholding, subspace pursuit methods.
The project uses Arduino UNO with multiple sensors attached to it like Soil Moisture sensor, Photoelectric Diode Sensor,Humidity sensor etc that takes reading on a periodic basis.The mission of the project is to intimate the farmers or other stakeholders in predicting when the crop is about to deteriorate. This is accomplished by a Machine Learning Layer that has been modeled to identify the degradation of a crop's health before its degradation starts. It uses KNN-algorithm to cluster the crops according to the impeding eventuality.The next iteration identifies the most probable cause and thorugha UI intimates the stakeholder whta steps needs to be taken to prevent crop-failure
A latent text-to-image diffusion model
A Novel Strategy for Minimizing Data Utility Loss
King's College London MSc Group Project Module 2016
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
A playbook for systematically maximizing the performance of deep learning models.
[ICLR'24 Spotlight] Uni3D: 3D Visual Representation from BAAI