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Hi, I'm Hua-zhang Hu 👋

😄 I’m a Master of SVIP-Lab in ShanghaiTech University, supervised by Shenghua Gao.

🔭 I mainly focus on:

  • Video Understanding and Activity Analysis
  • Multi-modality Learning

🌱 Publications:

  • Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos CVPR 2023.
    Paper | Code | Video
  • TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting CVPR 2022.
    Paper | Code | Oral Presentation

✨ Working:

  • 2024-05--Now: I am working as an AIGC engineer in Xiaohongshu.
  • 2023-06--2023-09: I worked as an intern in Alibaba.
  • 2022-11--2023-03: I worked as an intern in NIO.

💬 News:

  • 2023-03: A paper about multi-modality representation learning is accepted on CVPR 2023.
  • 2023-01: I serve as a reviewer for CVPR 2023, ICCV 2023.
  • 2022-06: We are invited to oral presentation with virtual attendance on CVPR 2022.
  • 2022-03: A paper about repetitive action counting is accepted for an Oral presentation on CVPR 2022.

📫 Welcome to communicate with me. Reach me through: [email protected]

Huazhang Hu's Projects

deeplearning icon deeplearning

The process of learn DeepLearning is a interesting and attractive trip

gcn-anomaly-detection icon gcn-anomaly-detection

Source codes of our paper in CVPR 2019: Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

lenet-5 icon lenet-5

It rebuilds LeNet-5 on cifar-10 dataset based on Tensorflow

openbilibili-go-common icon openbilibili-go-common

听说这是来自 https://github.com/openbilibili/go-common/ 的 “哔哩哔哩 bilibili 网站后台工程 源码”，不过咱也不知道这是啥。

opencv-api icon opencv-api

学习OpenCV-Python的一些学习代码，主要是一些API的调用和基本原理。Python版本 3.65，OpenCV版本3.3（推荐）

portscan icon portscan

基于Flask的TCP/UDP协议端口与服务自动化扫描

video-diffusion-pytorch icon video-diffusion-pytorch

Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch

videomae icon videomae

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

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