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+# ————2022.11.29 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
+
+# ————2022.12.30 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