Course Recap and What’s Next?

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Presentation transcript:

Course Recap and What’s Next?

Course Recap What did we learn this semester?

Image Processing and Manipulation Image Processing – Brightness, Saturation, Color Spaces Image Convolution / Filtering: Blurring, Sharpness, Noise Removal Image Gradients / Sobel Operators / Features

Basic Machine Learning and Image Classification Softmax Classifier / Linear Classifiers in General Stochastic Gradient Descent (SGD) Momentum, Regularization, Choosing Hyperparameters

Deep Learning Multi-layer Perceptron / Neural Networks The backpropagation algorithm Implementing our own Deep Learning Toolkit / Library

Deep Learning for Visual Recognition: Classification Alexnet, VGG Networks. Residual Connections, Resnets Google Net, Inception Nets

Deep Learning for Visual Recognition: Detection / Segmentation Convolutional Neural Networks (CNNs) Object Detection: RCNN, Fast-RCNN, Faster-RCNN, SSD, YOLO, SNIPER Image Segmentation: Deconvolutions, Fully Convolutional Networks, U-Nets.

Deep Learning for Visual Recognition: Synthesis Generating Images: Generative Adversarial Networks (GANs), Variational Auto-Encoder (VAEs)

Deep Learning for Visual Recognition: Other Tasks Human Pose Estimation: Hourglass Networks Matching: Siamese Networks, Triplet Losses

What else is coming? Image Classification Models have almost peaked on Imagenet Model search: How to find model architectures automatically!

What else is coming? Image Classification Models have almost peaked on Imagenet Model search: How to find model architectures automatically! These models are as fast as Resnet models but ~2% more accurate (79% vs 77%)

What else is coming? Object Detection CornerNets, CenterNets “CornerNet: Detecting Objects as Paired Keypoints” by Hei Law, and Jia Deng ECCV 2018

What else is coming? Object Detection CornerNets, CenterNets “CornerNet: Detecting Objects as Paired Keypoints” by Hei Law, and Jia Deng ECCV 2018

What else is coming? Object Detection CornerNets, CenterNets *CenterNet: Keypoint Triplets for Object Detection by Kaiwen Duan et al. April 17, 2019 arXiv Objects as Points. Xingyi Zhou, Dequan Wang, Philipp Krähenbühl. April 2019.

Interpretable Deep Learning This Looks Like That: Deep Learning for Interpretable Image Recognition. Chen et al 2018.

Here at UVA In Submission

Here at UVA How to learn Interpretable and Robust models? How to learn models that do not need as much training data? How to learn faster models? e.g. XNOR-Nets How to better represent multimodal features? (images + text) How to build compositional models for unusual images?

Thanks and All the Best!