2019 CV综述_cvcsdn

作者：Amusi
链接：https://www.zhihu.com/question/355566860/answer/894352980
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
 

目标检测

2019 四大目标检测综述论文：

Imbalance Problems in Object Detection: A Review

• intro: under review at TPAMI
• arXiv: https://arxiv.org/abs/1909.00169

Recent Advances in Deep Learning for Object Detection

• intro: From 2013 (OverFeat) to 2019 (DetNAS)
• arXiv: https://arxiv.org/abs/1908.03673

A Survey of Deep Learning-based Object Detection

• intro：From Fast R-CNN to NAS-FPN
• arXiv：https://arxiv.org/abs/1907.09408

Object Detection in 20 Years: A Survey

• intro：This work has been submitted to the IEEE TPAMI for possible publication
• arXiv：https://arxiv.org/abs/1905.05055

目标检测更多论文详见：https://github.com/amusi/awesome-object-detection

图像分割

Deep Semantic Segmentation of Natural and Medical Images: A Review

• intro: 从 FCN(2014) 到 Auto-DeepLab(2019)，本综述共含179篇语义分割和医学图像分割参考文献
• arXiv: https://arxiv.org/abs/1910.07655

Understanding Deep Learning Techniques for Image Segmentation

• intro: 本综述介绍了从2013年到2019年，主流的30多种分割算法（含语义/实例分割），50多种数据集，共计224篇参考文献
• arXiv: https://arxiv.org/abs/1907.06119

目标跟踪

A Review of Visual Trackers and Analysis of its Application to Mobile Robot

• intro: 本目标跟踪综述共含185篇参考文献！从传统方法到最新的深度学习网络
• arXiv: https://arxiv.org/abs/1910.09761

Deep Learning in Video Multi-Object Tracking: A Survey

• intro: 38页目标跟踪综述，含30多种主流算法，共计174篇参考文献
• arXiv: https://arxiv.org/abs/1907.12740

超分辨率

A Deep Journey into Super-resolution: A survey

• arXiv: https://arxiv.org/abs/1904.07523

Deep Learning for Image Super-resolution: A Survey

• arXiv:https://arxiv.org/abs/1902.06068

医学图像分割

Deep learning for cardiac image segmentation: A review

• intro: 本医学图像分割综述从FCN(2014)到Dense U-net(2019)，超过250篇的参考文献（论文中光画图的工作量就超级大）
• arXiv: https://arxiv.org/abs/1911.03723

Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

• arXiv: https://arxiv.org/abs/1911.02521

显著性目标检测

Salient Object Detection in the Deep Learning Era: An In-Depth Survey

• arXiv: https://arxiv.org/abs/1904.09146
• github: https://github.com/wenguanwang/SODsurvey

行为识别

Spatio-temporal Action Recognition: A Survey

• arXiv: https://arxiv.org/abs/1901.09403

深度估计

Monocular Depth Estimation: A Survey

• arXiv: https://arxiv.org/abs/1901.09402