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Zhiyu Zhao's Projects

algorithm_interview_notes-chinese icon 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 icon amd

[CVPR 2024] Asymmetric Masked Distillation for Pre-Training Small Foundation Models

ask-anything icon ask-anything

a simple yet interesting tool for chatting about video with chatGPT, miniGPT4 and StableLM

awesome-anything icon awesome-anything

General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX

cpd-video icon cpd-video

Learning Spatiotemporal Features via Video and Text Pair Discrimination

da-faster-rcnn-pytorch icon da-faster-rcnn-pytorch

An unofficial implementation of 'Domain Adaptive Faster R-CNN for Object Detection in the Wild ’

da_detection icon da_detection

Implementation of "Strong-Weak Distribution Alignment for Adaptive Object Detection"

deepspectralclustering icon deepspectralclustering

Pytorch Implemention of paper "Deep Spectral Clustering Learning", the state of the art of the Deep Metric Learning Paper

internvideo icon internvideo

InternVideo: General Video Foundation Models via Generative and Discriminative Learning (https://arxiv.org/abs/2212.03191)

modelarts-lab icon 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 icon py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

pygcn icon pygcn

Graph Convolutional Networks in PyTorch

rcnn icon rcnn

The Tensorflow with tflearn implementation of the RCNN model.

slowfast icon slowfast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

tome icon tome

A method to increase the speed and lower the memory footprint of existing vision transformers.

uestc_internet_plus_course_project icon 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 icon videomae

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

videomaev2 icon videomaev2

[CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

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