论文阅读杂记

序号	名称	备注
1	learning Efficient Convolutional Networks through Network Slimming	基于BN的模型剪枝
2	FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery	FAIR1M:卫星遥感下的细粒度数据集
3	Rich feature hierarchies for accurate object detection and semantic segmentation Tech report(RCNN)	RCNN
4	Fast RCNN	Fast RCNN
5	Faster RCNN	Faster RCNN
6	R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection	R2CNN:text的斜框检测
7	Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition(SPP-net)	SPP-net
8	ReDet:A Rotation-equivariant Detector for Aerial Object Detection	ReDet
9	You Only Look Once: Unified, Real-Time Object Detection	YOLO
10	YOLO9000: Better, Faster, Stronger	YOLOv2
11	YOLOv3: An Incremental Improvement	YOLOv3
12	YOLOv4: Optimal Speed and Accuracy of Object Detection	YOLOv4
13	Cascade R-CNN: Delving into High Quality Object Detection	Cascade RCNN
14	YOLOX: Exceeding YOLO Series in 2021	YOLOX
15	Aggregated Residual Transformations for Deep Neural Networks	ResNeXt
16	TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios	TPH-YOLOv5

序号	名称	备注
1	Very Deep Convolutional Networks for Large-Scale Image Recognition	VGG
2	OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks	OverFeat
3	ResNet:Deep Residual Learning for Image Recognition	ResNet
4	MobileNet	MobileNet

序号	名称	备注
1	Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition	Mask-CNN
2	FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery	FAIR1M:卫星遥感下的细粒度数据集
3	Selective Sparse Sampling for Fine-grained Image Recognition	S3N，稀疏注意力
4	Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization	FDL，RPN+知识蒸馏

序号	名称	备注
1	Residual Attention Network for Image Classification	ResAN
2	CBAM: Convolutional Block Attention Module	CBAM
3	BAM: Bottleneck Attention Module	BAM
4	An Attention Module for Convolutional Neural Networks	AW-Convlution