Computer Vision/Deep learning papers

Last year in 2018, I took couple of online courses to get me upto speed to the recent advances made in field of Deep learning.

Since, I was primarily interested in application of Deep learning, I picked Computer vision as a domain to apply these techniques. Deep application has numerous application like Text, Speech etc.

While taking these courses, I had the opportunity to read many papers published in this field from 2013. They varied from

• Deep learning vanilla architecture,
• Application of Computer vision in
  • classification
  • Detection,
  • Segmentation.

You can find the list of Papers I read here

Computer Vision Paper Reading

List of papers read un/read

• [x] Traffic Sign Recognition using Multi-scale convolution network
• [ ] Detecting Small Signs from Large Images
• [x] End to End Learning for Self Driving Cars
• [x] DeepPose: Human Pose Estimation via Deep Neural Networks
• [ ] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
• [x] R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation

Vanial Architecture

• [x] Network In Network
• [x] AlexNet: ImageNet Classification with Deep Convolutional Neural Networks
• [x] ZfNet: Visualizing and Understanding Convolutional Networks
• [x] VggNet: Very Deep Convolutional Networks for Large-Scale Image Recognition
• [x] ResNet: Deep Residual Learning for Image Recognition
• [x] GoogLenet v1 Going Deeper with Convolutions
• [x] Inception v3: Rethinking the Inception Architecture for Computer Vision
• [x] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
• [x] Densely Connected Convolutional Networks
• [x] Fast R-CNN
• [ ] Region of Intertest Pooling
• [x] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• [x] You Only Look Once:
  Unified, Real-Time Object Detection

Detection Task

• [x] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• [x] SSD: Single Shot MultiBox Detector

Segmentation

• [x] Semnatic Segmentation
• [ ] A Review on Deep Learning Techniques
  Applied to Semantic Segmentation
• [x] Fully Convolutional Networks for Semantic Segmentation
• [x] Learning Deconvolution Network for Semantic Segmentation
• [ ] A Review on Deep Learning Techniques
  Applied to Semantic Segmentation
• [ ] Simultaneous Detection and Segmentation
• [ ] Hypercolumns for Object Segmentation and Fine-grained Localization
• [ ] Convolutional Feature Masking for Joint Object and Stuff Segmentation
• [x] Instanse Segmentation
• [x] Instance-aware Semantic Segmentation via Multi-task Network Cascades
• [x] U-Net: Convolutional Networks for Biomedical Image Segmentation
• [x] Show and Tell: A Neural Image Caption Generator

Autonomous Driving

• [ ] Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness
• [x] Pushing the “Speed Limit”: High-Accuracy U.S. Traffic Sign Recognition with Convolutional Neural Networks

There are still many papers to read in domain of Unsupervised learning. Keep reading.