/CV-papers

Survey papers to follow in the Computer Vision area

CV-papers ( ᵔ ᴗ ᵔ )✌

Survey papers to follow in the Computer Vision area included object detection, image segmentation, object tracking, super resolution

object Detection

2019 Four object Detection Review Papers:

Imbalance Problems in Object Detection: A Review

  • intro: under review at TPAMI
  • arXiv

Recent Advances in Deep Learning for Object Detection

  • intro: From 2013 (OverFeat) to 2019 (DetNAS)
  • arXiv

A Survey of Deep Learning-based Object Detection

  • intro: From Fast R-CNN to NAS-FPN
  • arXiv

Object Detection in 20 Years: A Survey

  • intro: This work has been submitted to the IEEE TPAMI for possible publication
  • arXiv

More papers on object detection can be found at: github

Image segmentation

Deep Semantic Segmentation of Natural and Medical Images: A Review

  • intro: From FCN (2014) to Auto-DeepLab (2019), this review contains 179 semantic segmentation and medical image segmentation references
  • arXiv

Understanding Deep Learning Techniques for Image Segmentation

  • intro: This review introduces more than 30 mainstream segmentation algorithms (including semantic / instance segmentation) from 2013 to 2019, more than 50 data sets, a total of 224 references
  • arXiv

object Tracking

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

  • intro: This goal tracking review contains 185 references! From traditional methods to the latest deep learning networks
  • arXiv

Deep Learning in Video Multi-Object Tracking: A Survey

  • intro: 38-page overview of object tracking, including more than 30 mainstream algorithms, with a total of 174 references
  • arXiv

Super resolution

A Deep Journey into Super-resolution: A survey

Deep Learning for Image Super-resolution: A Survey

Medical image segmentation

Deep learning for cardiac image segmentation: A review

  • intro: This review of medical image segmentation from FCN (2014) to Dense U-net (2019), more than 250 references (the workload of light drawing in the paper is super large)
  • arXiv

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

Saliency object detection

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

Behavior recognition

Spatio-temporal Action Recognition: A Survey

Depth estimation

Monocular Depth Estimation: A Survey