# 2022.11 CS231n学习汇报
    介绍CS231n部分的学习，包含计算机视觉简介、图像分类（K最邻近算法、线性分类）、神经网络以及卷积神经网络部分

# 2022.12 行人重识别任务汇报
    关于行人重识别第一个阶段的学习汇报

# 2023.9 行人检测汇报
    关于行人检测系列论文汇报汇总
* Co-Scale Conv-Attentional Image Transformers
* LEAPS: End-to-End One-Step Person Search With Learnable Proposals
* Cascade Transformers for End-to-End Person Search
* PSTR: End-to-End One-Step Person Search With Transformers
* FCOS: Fully Convolutional One-Stage Object Detection
* Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection

# 2024.1 相关论文阅读汇报
    包含其他方面结合MAE部分的汇报
* Generic-to-Specific Distillation of Masked Autoencoders

# 2024.3 2024春季论文汇报
    包含MAE有关工作及文章部分汇总
* Masked Autoencoders Are Scalable Vision Learners
* BEiT: BERT Pre-Training of Image Transformers
* BEIT V2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
* Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
* HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
* MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
* MultiMAE: Multi-modal Multi-task Masked Autoencoders
* ConvMAE: Masked Convolution Meets Masked Autoencoders
* RetroMAE: Pre-training Retrieval-oriented Transformers via Masked Auto-Encoder
* Siamese Masked Autoencoders

* Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
* Integral Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection

* Masked Image Modeling with Local Multi-Scale Reconstruction
* VideoMAE: Masked Autoencoders are Data-Efficient Learners for  Self-Supervised Video Pre-Training
* VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking