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.
Этот курс входит в специализацию ''Специализация Продвинутое машинное обучение'
Deep Learning in Computer Vision
от партнера
Специализация Продвинутое машинное обучениеНациональный исследовательский университет "Высшая школа экономики"
Программа курса: что вы изучите
Неделя
13 ч. на завершение
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 видео ((всего 54 мин.)), 2 тестов
8 видео
Digital images3мин
Structure of human eye and vision6мин
Color models15мин
Image processing goals and tasks2мин
Contrast and brightness correction5мин
Image convolution7мин
Edge detection8мин
1 практическое упражнение
Basic image processing10мин
Неделя
24 ч. на завершение
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 видео ((всего 91 мин.)), 2 тестов
12 видео
AlexNet, VGG and Inception architectures11мин
ResNet and beyond10мин
Fine-grained image recognition5мин
Detection and classification of facial attributes6мин
Content-based image retrieval7мин
Computing semantic image embeddings using convolutional neural networks8мин
Employing indexing structures for efficient retrieval of semantic neighbors9мин
Face verification6мин
The re-identification problem in computer vision5мин
Facial keypoints regression6мин
CNN for keypoints regression5мин
1 практическое упражнение
Convolutional features for visual recognition24мин
Неделя
33 ч. на завершение
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 видео ((всего 46 мин.)), 2 тестов
13 видео
Sliding windows3мин
HOG-based detector2мин
Detector training3мин
Viola-Jones face detector5мин
Attentional cascades and neural networks3мин
Region-based convolutional neural network3мин
From R-CNN to Fast R-CNN5мин
Faster R-CNN4мин
Region-based fully-convolutional network2мин
Single shot detectors3мин
Speed vs. accuracy tradeoff1мин
Fun with pedestrian detectors1мин
1 практическое упражнение
Object Detection16мин
Неделя
44 ч. на завершение
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 видео ((всего 74 мин.)), 2 тестов
11 видео
Optical flow5мин
Deep learning in optical flow estimation5мин
Visual object tracking5мин
Examples of visual object tracking methods13мин
Multiple object tracking5мин
Examples of multiple object tracking methods8мин
Introduction to action recognition6мин
Action classification7мин
Action classification with convolutional neural networks5мин
Action localization6мин
1 практическое упражнение
Video Analysis16мин
Неделя
53 ч. на завершение
Image segmentation and synthesis
In the last module of this course, we shall consider problems where the goal is to predict entire image. These are semantic image segmentation and image synthesis problems. Modern CNNs tailored for segmentation employ multiple specialised layers to allow for efficient training and inference. Lastly, we will get to know Generative Adversarial Networks — a bright new idea in machine learning, allowing to generate arbitrary realistic images.
7 видео ((всего 43 мин.)), 2 тестов
7 видео
Oversegmentation4мин
Deep learning models for image segmentation7мин
Human pose estimation as image segmentation8мин
Style transfer5мин
Generative adversarial networks7мин
Image transformation with neural networks5мин
1 практическое упражнение
Image segmentation and synthesis14мин
3.8
Рецензии: 30Лучшие рецензии
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.
Преподаватели
Anton Konushin
Senior LecturerHSE Faculty of Computer Science
Alexey Artemov
Senior LecturerHSE Faculty of Computer Science
О Национальный исследовательский университет "Высшая школа экономики"
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
О специализации ''Продвинутое машинное обучение'
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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Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
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