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本文仅用于学术交流,侵删
https://www.zhihu.com/question/355566860
1、作者:Amusi
https://www.zhihu.com/question/355566860/answer/894352980
一直关注CV这一块,下面分享几个2019年比较好的CV综述,方向涵盖:目标检测、图像分割、目标跟踪和超分辨率等
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
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
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
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
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
2、作者:魏秀参
https://www.zhihu.com/question/355566860/answer/896661195
自荐一篇“Deep Learning for Fine-Grained Image Analysis: A Survey“:
《超全深度学习细粒度图像分析:项目、综述、教程一网打尽》
链接:
https://mp.weixin.qq.com/s/2pJt9hlUFhR6mo1ughKkiA
另,除文末提及的几个具体future directions
Automatic Fine-Grained Models
Fine-Grained Few-Shot Learning
Fine-Grained Hashing
之外。实际上FGIA领域还有非常多新鲜好玩的问题和应用值得探索,如:
我们围绕FGIA提出的一个目前最大的新零售场景商品数据集RPC:
https://zhuanlan.zhihu.com/p/55627416
在真实细粒度识别场景中不可避免的长尾分布问题:Long tailed problems
存在跨域差异(Domain adaptation)的细粒度图像识别和检索
……
期待更多CVer在FGIA领域作出有影响力的工作,更多FGIA信息可参见:
http://www.weixiushen.com/tutorials.html
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*延伸阅读
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