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Higher School of Economics

Deep Learning in Computer Vision

Higher School of Economics via Coursera

This course may be unavailable.


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 online 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 the 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 often demonstrated in movies and TV-shows example of computer vision and AI.

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  • 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!
  • 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.
  • 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.
  • 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.
  • 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.

Taught by

Anton Konushin and Alexey Artemov


2.3 rating, based on 3 Class Central reviews

Start your review of Deep Learning in Computer Vision

  • Anonymous
    Techniques discussed in the course are state of the art and are presented in a very engaging manner. The highlight of the course are the assignments which, unlike many courses I've seen, give a lot of freedom as per coding methodology. You'll implement basic edge detectors from scratch, perform image correction, keypoints regression, face detection and recognition and you'll also implement image generation!
  • Anonymous
    The teachers are really bad. They are just reading there transcripts or notes. So although the assignments are good but theory is really poor and incomplete.
  • Profile image for Jakub Bartczuk
    Jakub Bartczuk
    Pros: -thorough course material -course teaches older methods in addition to deep learning - most CV with DL courses don't do that -ambitious and interesting assignments Cons: -there isn't ANY assistance from the instructors or the TA. If you c…

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