Project seminar "Deep learning for computer vision"

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

9

Кредиты

Кто читает:

Кафедра прикладной математики и информатики (Нижний Новгород) (Факультет информатики, математики и компьютерных наук (Нижний Новгород))

Статус:

Курс обязательный

Когда читается:

1-й курс, 4 модуль

Преподаватель

Савченко Андрей Владимирович

Course Syllabus

Abstract

This course covers basics of Deep Learning approaches applied to the popular Computer Vision tasks. The aim of the course is to highlight how the modern 2D Computer Vision works and how the area evolved to this. Students will learn the main concepts of the modern 2D Computer Vision, how to master some popular applied tasks and try by themselves. Every week, the students will learn a standalone chapter of Computer Vision starting from the Image Classification approaches, through Object Detection, Image Segmentation and ending with some cutting-edge and real-life approaches. The students also will introduced to the popular datasets for each task along with the common quality estimation procedures and open source solutions.

Learning Objectives

• The aim of the course is to highlight how the modern 2D Computer Vision works and how the area evolved to this.

Expected Learning Outcomes

• Students will touch with modern Deep Learning frameworks
• Students will build their first neural networks models
• ● Students will learn how to use pretrained image classifiers and build their own
• ● Students will learn how to use pretrained object detector and build their own
• ● Students will learn how to use existing REID models and build their own
• ● Students will learn how to track their objects
• Students will learn how to use pretrained image segmentation model and uild their own
• Students willattempt to create a prototype of real-life video surveillance solution

Course Contents

• Introduction to Deep Learning
• Deep Learning for Image Classification
• Deep Object Detection
• Deep Object Tracking and Person Reidentification
• Deep Image Segmentation
• Final project

Assessment Elements

• test week 1-4
• Programming assingment
• Final exam

Interim Assessment

• 2022/2023 4th module
  0.2 * test week 1-4 + 0.4 * Programming assingment + 0.4 * Final exam

Bibliography

Recommended Core Bibliography

• Beysolow, T. (2018). Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892182
• Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019). A Survey of Deep Learning-based Object Detection. https://doi.org/10.1109/ACCESS.2019.2939201
• Tareq Khan. (2019). A Deep Learning Model for Snoring Detection and Vibration Notification Using a Smart Wearable Gadget. Electronics, (9), 987. https://doi.org/10.3390/electronics8090987

Recommended Additional Bibliography

• Deep learning, Kelleher, J. D., 2019
• Introduction to deep learning, Charniak, E., 2018