文献链接

图像分类(Classification)

• LeNet http://yann.lecun.com/exdb/lenet/index.html
• AlexNet http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
• ZFNet(Visualizing and Understanding Convolutional Networks) https://arxiv.org/abs/1311.2901
• VGG https://arxiv.org/abs/1409.1556
• GoogLeNet, Inceptionv1(Going deeper with convolutions) https://arxiv.org/abs/1409.4842
• Batch Normalization https://arxiv.org/abs/1502.03167
• Inceptionv3(Rethinking the Inception Architecture for Computer Vision) https://arxiv.org/abs/1512.00567
• Inceptionv4, Inception-ResNet https://arxiv.org/abs/1602.07261
• Xception(Deep Learning with Depthwise Separable Convolutions) https://arxiv.org/abs/1610.02357
• ResNet https://arxiv.org/abs/1512.03385
• ResNeXt https://arxiv.org/abs/1611.05431
• DenseNet https://arxiv.org/abs/1608.06993
• NASNet-A(Learning Transferable Architectures for Scalable Image Recognition) https://arxiv.org/abs/1707.07012
• SENet(Squeeze-and-Excitation Networks) https://arxiv.org/abs/1709.01507
• MobileNet(v1) https://arxiv.org/abs/1704.04861
• MobileNet(v2) https://arxiv.org/abs/1801.04381
• MobileNet(v3) https://arxiv.org/abs/1905.02244
• ShuffleNet(v1) https://arxiv.org/abs/1707.01083
• ShuffleNet(v2) https://arxiv.org/abs/1807.11164
• Bag of Tricks for Image Classification with Convolutional Neural Networks https://arxiv.org/abs/1812.01187
• EfficientNet(v1) https://arxiv.org/abs/1905.11946
• EfficientNet(v2) https://arxiv.org/abs/2104.00298
• CSPNet https://arxiv.org/abs/1911.11929
• RegNet https://arxiv.org/abs/2003.13678
• NFNets(High-Performance Large-Scale Image Recognition Without Normalization) https://arxiv.org/abs/2102.06171
• Vision Transformer https://arxiv.org/abs/2010.11929
• DeiT(Training data-efficient image transformers ) https://arxiv.org/abs/2012.12877
• Swin Transformer https://arxiv.org/abs/2103.14030
• Swin Transformer V2: Scaling Up Capacity and Resolution https://arxiv.org/abs/2111.09883
• BEiT: BERT Pre-Training of Image Transformers https://arxiv.org/abs/2106.08254
• MAE(Masked Autoencoders Are Scalable Vision Learners) https://arxiv.org/abs/2111.06377
• ConvNeXt(A ConvNet for the 2020s) https://arxiv.org/abs/2201.03545
• MobileViT V1 https://arxiv.org/abs/2110.02178
• MobileViT V2(Separable Self-attention for Mobile Vision Transformers) https://arxiv.org/abs/2206.02680
• MobileOne(An Improved One millisecond Mobile Backbone) https://arxiv.org/abs/2206.04040

目标检测(Object Detection)

• R-CNN https://arxiv.org/abs/1311.2524
• Fast R-CNN https://arxiv.org/abs/1504.08083
• Faster R-CNN https://arxiv.org/abs/1506.01497
• Cascade R-CNN: Delving into High Quality Object Detection https://arxiv.org/abs/1712.00726
• Mask R-CNN https://arxiv.org/abs/1703.06870
• SSD https://arxiv.org/abs/1512.02325
• FPN(Feature Pyramid Networks for Object Detection) https://arxiv.org/abs/1612.03144
• RetinaNet(Focal Loss for Dense Object Detection) https://arxiv.org/abs/1708.02002
• Bag of Freebies for Training Object Detection Neural Networks https://arxiv.org/abs/1902.04103
• YOLOv1 https://arxiv.org/abs/1506.02640
• YOLOv2 https://arxiv.org/abs/1612.08242
• YOLOv3 https://arxiv.org/abs/1804.02767
• YOLOv4 https://arxiv.org/abs/2004.10934
• YOLOX(Exceeding YOLO Series in 2021) https://arxiv.org/abs/2107.08430
• YOLOv7 https://arxiv.org/abs/2207.02696
• PP-YOLO https://arxiv.org/abs/2007.12099
• PP-YOLOv2 https://arxiv.org/abs/2104.10419
• CornerNet https://arxiv.org/abs/1808.01244
• FCOS(Old) https://arxiv.org/abs/1904.01355
• FCOS(New) https://arxiv.org/abs/2006.09214
• CenterNet https://arxiv.org/abs/1904.07850

语义分割(Semantic Segmentation)

• FCN(Fully Convolutional Networks for Semantic Segmentation) https://arxiv.org/abs/1411.4038
• UNet(U-Net: Convolutional Networks for Biomedical Image Segmentation) https://arxiv.org/abs/1505.04597
• DeepLabv1(Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs) https://arxiv.org/abs/1412.7062
• DeepLabv2(Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs) https://arxiv.org/abs/1606.00915
• DeepLabv3(Rethinking Atrous Convolution for Semantic Image Segmentation) https://arxiv.org/abs/1706.05587
• DeepLabv3+(Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation) https://arxiv.org/abs/1802.02611
• SegFormer https://arxiv.org/abs/2105.15203

显著性目标检测(Salient Object Detection)

• U2Net https://arxiv.org/abs/2005.09007

实例分割(Instance Segmentation)

• Mask R-CNN https://arxiv.org/abs/1703.06870

关键点检测(Keypoint Detection)

• HRNet(Deep High-Resolution Representation Learning for Human Pose Estimation) https://arxiv.org/abs/1902.09212

网络量化(Quantization)

• Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference https://arxiv.org/abs/1712.05877
• Quantizing deep convolutional networks for efficient inference: A whitepaper https://arxiv.org/abs/1806.08342
• Data-Free Quantization Through Weight Equalization and Bias Correction https://arxiv.org/abs/1906.04721
• LSQ: Learned Step Size Quantization https://arxiv.org/abs/1902.08153
• LSQ+: Improving low-bit quantization through learnable offsets and better initialization https://arxiv.org/abs/2004.09576

自然语言处理

• Attention Is All You Need https://arxiv.org/abs/1706.03762

Others

• Microsoft COCO: Common Objects in Context https://arxiv.org/abs/1405.0312
• The PASCALVisual Object Classes Challenge: A Retrospective http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham15.pdf
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization https://arxiv.org/abs/1610.02391