Lecture 15 introduces object detection as the core computer vision task of localizing objects in images. We contrast this task with the image classification task we have mostly discussed so far, and see how we need new datasets (PASCAL, COCO), evaluation metrics (IoU, mAP), and neural network architectures to tackle this new task. We discuss the twostage RCNN family of methods for object detection including “slow” RCNN, Fast RCNN, and Faster RCNN, and also briefly discuss singlestage approaches to object detection.

Slides: http://myumi.ch/r80Pz
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Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and selfdriving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these stateoftheart visual recognition systems. This course is a deep dive into details of neuralnetwork based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cuttingedge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and finetuning networks for visual recognition tasks.

Course Website: http://myumi.ch/Bo9Ng

Instructor: Justin Johnson http://myumi.ch/QA8Pg