Lecture 11 | Detection and Segmentation

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In Lecture 11 we move beyond image classification, and show how convolutional networks can be applied to other core computer vision tasks. We show how fully convolutional networks equipped with downsampling and upsampling layers can be used for semantic segmentation, and how multitask losses can be used for localization and pose estimation. We discuss a number of methods for object detection, including the region-based R-CNN family of methods and single-shot methods like SSD and YOLO. Finally we show how ideas from semantic segmentation and object detection can be combined to perform instance segmentation.

Keywords: Semantic segmentation, fully convolutional networks, unpooling, transpose convolution, localization, multitask losses, pose estimation, object detection, sliding window, region proposals, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, DenseCap, instance segmentation, Mask R-CNN

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf


Convolutional Neural Networks for Visual Recognition

Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.


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