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
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Masked Autoencoders Are Scalable Vision Learners
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BEiT: BERT Pre-Training of Image Transformers
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BEIT V2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
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Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
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HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
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MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
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MultiMAE: Multi-modal Multi-task Masked Autoencoders
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ConvMAE: Masked Convolution Meets Masked Autoencoders
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RetroMAE: Pre-training Retrieval-oriented Transformers via Masked Auto-Encoder
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Siamese Masked Autoencoders
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Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
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Integral Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
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Masked Image Modeling with Local Multi-Scale Reconstruction
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VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking