Zhiyu Zhao's repositories
faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
VideoMAEv2
[CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/自然语言处理(NLP)/深度学习(Deep Learning)/机器学习(Machine Learning)/C/C++/Python/面试笔记，此外，还包括创建者看到的所有机器学习/深度学习面经中的问题。 除了其中 DL/ML 相关的，其他与算法岗相关的计算机知识也会记录。 但是不会包括如前端/测试/JAVA/Android等岗位中有关的问题。
AMD
[CVPR 2024] Asymmetric Masked Distillation for Pre-Training Small Foundation Models
Ask-Anything
a simple yet interesting tool for chatting about video with chatGPT, miniGPT4 and StableLM
Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
da-faster-rcnn-PyTorch
An unofficial implementation of 'Domain Adaptive Faster R-CNN for Object Detection in the Wild ’
DA_Detection
Implementation of "Strong-Weak Distribution Alignment for Adaptive Object Detection"
DeepSpectralClustering
Pytorch Implemention of paper "Deep Spectral Clustering Learning", the state of the art of the Deep Metric Learning Paper
Fast-RCNN-1
My implementation of Fast-RCNN （tensorflow）
image-segmentation
A graph-based image segmentation algorithm
InternVideo
InternVideo: General Video Foundation Models via Generative and Discriminative Learning (https://arxiv.org/abs/2212.03191)
ModelArts-Lab
ModelArts开发者交流互动平台，@ModelArts服务官网：https://www.huaweicloud.com/product/modelarts.html
py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
RCNN
The Tensorflow with tflearn implementation of the RCNN model.
SimpleMachineLearning
Machine learning is supposed to be simple~
SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
ToMe
A method to increase the speed and lower the memory footprint of existing vision transformers.
transferlearning
Everything about Transfer Learning and Domain Adaptation--迁移学习
uestc_Internet_plus_course_project
本人在大学期间所有课程课设和作业的代码和部分报告，包括【计算机组成与结构】、【计算机网络与通信技术】、【软件基础综合课程设计】、【互联网软件开发综合课程设计】、【数据挖掘与大数据分析】、【时间序列分析】、【机器学习】、【数据结构与算法】、【并行程序设计导论】、【计算机操作系统】、【计算机视觉】
VideoMAE
[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training