图像领域的经典文献汇总

1. 图像分类(Classification)

• AlexNet网络（http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf）
• ZFNet（Visualizing and Understanding Convolutional Networks）（http://arxiv.org/abs/1311.2901）
• VGG网络（http://arxiv.org/abs/1409.1556）
• GoogLeNet和Inceptionv1（Going deeper with convolutions）（http://arxiv.org/abs/1409.4842）
• 批归一化（Batch Normalization）（http://arxiv.org/abs/1502.03167）
• Inceptionv3（Retrying the Inception Architecture for Computer Vision）（http://arxiv.org/abs/1512.00567）
• Inceptionv4和Inception-ResNet（http://arxiv.org/abs/1602.07261）
• Xception：基于深度可分离卷积的深度学习（Deep Learning with Depthwise Separable Convolutions）（http://arxiv.org/abs/1610.02357）
• ResNet网络（http://arxiv.org/abs/1512.03385）
• ResNeXt网络（http://arxiv.org/abs/1611.05431）
• 稠密网：密集连接网络（DenseNet）（http://arxiv.org/abs/1608.06993）
• NASNet-A：可扩展图像识别的学习可转移架构（Learning Transferable Architectures for Scalable Image Recognition） （ http : // arXiv . org / abs / 7 ？ 7 ？ ？ ? https://arxiv.org-abs-?p=?
  等

---

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
• 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
• PP-YOLO https://arxiv.org/abs/2007.12099
• PP-YOLOv2 https://arxiv.org/abs/2104.10419

目标分割(Segmentation)

• Fully Convolutional Network (FCN) https://arxiv.org/abs/1411.4038
  • U-Net (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

自然语言处理

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

Others

• Microsoft Research's COCO: Contextual Common Objects https://arxiv.org/abs/1405.0312
• The Pascal Visual Object Class Challenge: A Retrospectives http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham15.pdf