Introduction to Computer Vision
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This presentation introduces the topic of computer vision, especially through the lense of Deep Learning. Go build! https://gluon-cv.mxnet.io Slides: Thomas Delteil
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Introduction to Computer Vision
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- 2. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
- 3. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Image Classification Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2015
- 4. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Object detection Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, 2016
- 5. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Semantic Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp Fischer, Thomas Brox, 2015
- 6. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Instance Segmentation Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick, 2017
- 7. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Object tracking Fully-Convolutional Siamese Networks for Object Tracking Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr, 2016
- 8. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Pose Estimation Simple Baselines for Human Pose Estimation and Tracking Bin Xiao, Haiping Wu, Yichen Wei, 2018
- 9. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Action Recognition Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification Ali Diba et al., 2017
- 10. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Super Resolution Super Resolution Bilinear Interpolation Photo-Realistic Single Image Super-Resolution Using a GAN, Ledig et al. 2016
- 11. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Image Restoration Deep Image Prior Dmitry Ulyanov, Yandex Andrea Vedaldi, Victor Lempitsk, 2017
- 12. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Visual Anomaly Detection Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity Paolo Napoletano, et al., 2017
- 13. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Scene Reconstruction CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction Keisuke Tateno, Federico Tombari, Iro Laina, Nassir Navab
- 14. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Image Generation Progressive Growing of GANs for Improved Quality, Stability, and Variation Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, 2018
- 15. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Optical Character Recognition Gated convolutional recurrent neural networks for multilingual handwriting recognition. Bluche, T. and Messina, R. (2017)
- 16. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Image Captioning Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Qi Wu, Chunhua Shen, Anton van den Hengel, Peng Wang, Anthony Dick (2016)
- 17. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Common denominator among all these tasks? All state-of-the-art results are achieved through the use of Deep Learning
Editor's Notes
- First call deck for a high level introduction to Apache MXNet.
- CIFAR10 Dataset
- Credit: https://www.flickr.com/photos/yoshikazut/25546580673/ under CC
- Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity | Paolo Napoletano, et al. |