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Yong Wu's Projects

fanet icon fanet

A repo for 360-degree omni-directional image SOD

faze_preprocess icon faze_preprocess

Preprocessing pipeline for the MPIIGaze and GazeCapture datasets for evaluations for Faze

few_shot_gaze icon few_shot_gaze

Pytorch implementation and demo of FAZE: Few-Shot Adaptive Gaze Estimation (ICCV 2019, oral)

fsl_ssl icon fsl_ssl

Code for "When Does Self-supervision Improve Few-shot Learning?" ECCV 2020

gocor icon gocor

Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network'

hypernetworks icon hypernetworks

PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)

jigendg icon jigendg

Repository for the CVPR19 oral paper "Domain Generalization by Solving Jigsaw Puzzles"

maml-pytorch icon maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

mast icon mast

MAST: A Memory-Augmented Self-supervised Tracker (CVPR 2020)

maxl icon maxl

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

metadistil icon metadistil

Code for the paper "Meta Learning for Knowledge Distillation".

metaoptnet icon metaoptnet

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

ml-visuals icon ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

orbit-dataset icon orbit-dataset

The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.

pygaze icon pygaze

A general python framework for gaze estimation, based on PyTorch.

pytorch-byol icon pytorch-byol

PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

pytorch-maml icon pytorch-maml

A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.

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