Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.
The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.
提供方
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Introduction to image processing and computer vision
Welcome to the "Deep Learning for Computer Vision“ course! In the first introductory week, you'll learn about the purpose of computer vision, digital images, and operations that can be applied to them, like brightness and contrast correction, convolution and linear filtering. These simple image processing methods solve as building blocks for all the deep learning employed in the field of computer vision. Let’s get started!
8 个视频 （总计 54 分钟）, 2 个测验
8 个视频
Digital images3分钟
Structure of human eye and vision6分钟
Color models15分钟
Image processing goals and tasks2分钟
Contrast and brightness correction5分钟
Image convolution7分钟
Edge detection8分钟
1 个练习
Basic image processing10分钟
周
2完成时间为 4 小时
Convolutional features for visual recognition
Module two revolves around general principles underlying modern computer vision architectures based on deep convolutional neural networks. We’ll build and analyse convolutional architectures tailored for a number of conventional problems in vision: image categorisation, fine-grained recognition, content-based retrieval, and various aspect of face recognition. On the practical side, you’ll learn how to build your own key-points detector using a deep regression CNN.
12 个视频 （总计 91 分钟）, 2 个测验
12 个视频
AlexNet, VGG and Inception architectures11分钟
ResNet and beyond10分钟
Fine-grained image recognition5分钟
Detection and classification of facial attributes6分钟
Content-based image retrieval7分钟
Computing semantic image embeddings using convolutional neural networks8分钟
Employing indexing structures for efficient retrieval of semantic neighbors9分钟
Face verification6分钟
The re-identification problem in computer vision5分钟
Facial keypoints regression6分钟
CNN for keypoints regression5分钟
1 个练习
Convolutional features for visual recognition24分钟
周
3完成时间为 3 小时
Object detection
In this week, we focus on the object detection task — one of the central problems in vision. We start with recalling the conventional sliding window + classifier approach culminating in Viola-Jones detector. Tracing the development of deep convolutional detectors up until recent days, we consider R-CNN and single shot detector models. Practice includes training a face detection model using a deep convolutional neural network.
13 个视频 （总计 46 分钟）, 2 个测验
13 个视频
Sliding windows3分钟
HOG-based detector2分钟
Detector training3分钟
Viola-Jones face detector5分钟
Attentional cascades and neural networks3分钟
Region-based convolutional neural network3分钟
From R-CNN to Fast R-CNN5分钟
Faster R-CNN4分钟
Region-based fully-convolutional network2分钟
Single shot detectors3分钟
Speed vs. accuracy tradeoff1分钟
Fun with pedestrian detectors1分钟
1 个练习
Object Detection16分钟
周
4完成时间为 4 小时
Object tracking and action recognition
The fourth module of our course focuses on video analysis and includes material on optical flow estimation, visual object tracking, and action recognition. Motion is a central topic in video analysis, opening many possibilities for end-to-end learning of action patterns and object signatures. You will learn to design computer vision architectures for video analysis including visual trackers and action recognition models.
11 个视频 （总计 74 分钟）, 2 个测验
11 个视频
Optical flow5分钟
Deep learning in optical flow estimation5分钟
Visual object tracking5分钟
Examples of visual object tracking methods13分钟
Multiple object tracking5分钟
Examples of multiple object tracking methods8分钟
Introduction to action recognition6分钟
Action classification7分钟
Action classification with convolutional neural networks5分钟
Action localization6分钟
1 个练习
Video Analysis16分钟
3.9
24 个审阅热门审阅
Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.
Don't just read what's written on the projector. Try explaining it. And explain with code.
讲师
Anton Konushin
Senior LecturerHSE Faculty of Computer Science
Alexey Artemov
Senior LecturerHSE Faculty of Computer Science
关于 国立高等经济大学
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.
Learn more on www.hse.ru
关于 高级机器学习 专项课程
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
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