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Yabin Zhang's Projects

advstyle icon advstyle

Adversarial Style Augmentation for Domain Generalization

auxselftrain icon auxselftrain

Gradual Domain Adaptation via Self-Training of Auxiliary Models

awesome-cv icon awesome-cv

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

bridging_uda_ssl icon bridging_uda_ssl

The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

cdan icon cdan

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

cdan-re-implement icon cdan-re-implement

The re-implement of NIPS 2018 paper: Conditional Adversarial Domain Adaptation

dassl.pytorch icon dassl.pytorch

A PyTorch toolbox for domain adaptation and semi-supervised learning.

dmn icon dmn

CVPR2024: Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models

efdm icon efdm

Official PyTorch codes of CVPR2022 Oral: Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization

fairseq icon fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

fixmatch-pytorch icon fixmatch-pytorch

Unofficial Pytorch code for "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" in NeurIPS'20. This repo contains reproduced checkpoints.

graph-rcnn.pytorch icon graph-rcnn.pytorch

Pytorch code for our ECCV 2018 paper "Graph R-CNN for Scene Graph Generation" and other papers

masksurf icon masksurf

Masked Surfel Prediction for Self-Supervised Point Cloud Learning

point-bert icon point-bert

[CVPR 2022] Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

point-dae icon point-dae

Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

point-mae icon point-mae

[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning

vpt icon vpt

❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119

ybzh.github.io icon ybzh.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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