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目录
论文阅读指南
高效阅读方法及流程
如何阅读论文
论文速读十问
读论文与口头报告的几项重点
参考材料
论文阅读清单
神经网络基础(basis)
注意力部分(attention)
批量&正则化(batch&normalization)
图像分类(CLAS)
高级卷积网络知识(Convolutional)
AI合成部分(GAN)
自然语言处理(NLP)
目标检测(OBJ)
循环神经网络(RNN)
图像分割(SEG)
Transformer
多模态(MultiModal Learning)
大语言模型(Large Language Models)
论文阅读笔记
MultiModal Machine Learning
BLIP: Bootstrapping Language-Image Pre-training
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Large Language Models
OPT: OPT : Open Pre-trained Transformer Language Models
GPT-v1:Improving Language Understanding by Generative Pre-Training
GPT-v2:Language Models are Unsupervised Multitask Learners
GPT-v3:Language Models are Few-Shot Learners
GPT-v4:GPT-4 Technical Report
Object Detection
summary
RCNN
Fast R-CNN
Faster R-CNN
Mask R-CNN
FCN
R-FCN
FPN
FCOS
SSD
Mobilenet-SSDv2
VarifocalNet
OneNet
Mask R-CNN
Cascade-RCNN
RetinaNet
FemtoDet
SparseInst
YOLOv1
YOLOv2
YOLOv3
YOLOv4
Scaled-YOLOv4
Edge-YOLO
MS-DAYOLO
ASFF
ATSS
SABL
SM-NAS
TSD
RDSNet
CenterMask
EfficientDet
Simple Multi-dataset Detection
YOLOX
YOLOv6
PP-YOLOv1
PP-YOLOv2
PP-YOLOE
YOLOF
YOLOP
YOLOR
YOLOS
YOLOv7
DY-yolov7
Gold-YOLO
YOLOv6
DAMO-YOLO
ViT-YOLO
YOLO-MS
DETR
RT-DETR
YOLOv9
YOLOOC
FemtoDet
MS-DAYOLO
OneNet
Sparse R-CNN
SparseInst
OWL-ViT
OWLv2
RTMDet
YOLO-World
YOLOOC
MDETR
YOLOv10
目标检测二十年：一项综述
YOLO的全面综述：从YOLOv1到YOLOv8及未来
论文阅读记录
论文阅读总结
Repository
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论文阅读总结
论文阅读总结
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