Lecture 2B: Computer Vision Applications (Full Stack Deep Learning - Spring 2021)

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--------------------------------------------------------------------------------------------- In this video, we will review notable applications of deep learning in computer vision. First, we will tour some ConvNet architectures. Then, we will talk about localization, detection, and segmentation problems. We will conclude with more advanced methods.

00:00 - Introduction
02:51 - AlexNet
05:09 - ZFNet
06:54 - VGGNet
09:06 - GoogLeNet
11:57 - ResNet
15:15 - SqueezeNet
17:05 - Architecture Comparisons
20:00 - Localization, Detection, and Segmentation Tasks
24:00 - Overfeat, YOLO, and SSD Methods
28:01 - Region Proposal Methods (R-CNN, Faster R-CNN, Mask R-CNN, U-Net)
34:33 - Advanced Tasks (3D Shape Inference, Face Landmark Recognition, and Pose Estimation)
37:00 - Adversarial Attacks
40:56 - Style Transfer

[nishantyadav]

This lecture can be a review paper on CNNs in computer vision. Very nicely done!