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Informatique et Mathématiques appliquées
Une voie, plusieurs choix

> Formation > Cursus ingénieur

Category learning & object recognition - WMM9MO31

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  • Number of hours

    • Lectures : 18.0
    • Tutorials : -
    • Laboratory works : -
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 3.0
  • Officials : Karteek ALAHARI

Goals

This course presents the evolution of learning methods for recognition over the past two decades, starting with techniques using hand-crafted features to modern feature learning algorithms.

Content

Methods for several problems in recognition, such as semantic segmentation, object detection, action detection, etc., will be discussed in detail.

The topics discussed include:

  • Features for recognition
  • Matching algorithms
  • Clustering methods
  • Generative and discriminative models
  • Bag-of-features for retrieval and classification
  • Graphcuts
  • Deep learning
  • Action recognition

Tests

Quiz organized during the lecture sessions and a final exam.

N1=50%EXAM1+50%QUIZ/CC
N2=50%EXAM2+50%QUIZ/CC

The exam is given in english only FR

Calendar

The course exists in the following branches:

  • Curriculum - Master 2 in Computer Science - Semester 9 (this course is given in english only EN)
  • Curriculum - Master 2 in Applied Mathematics - Semester 9 (this course is given in english only EN)
  • Curriculum - Master 2 in Computer Science - Semester 9 (this course is given in english only EN)
see the course schedule for 2020-2021

Additional Information

Course ID : WMM9MO31
Course language(s): FR

You can find this course among all other courses.

Bibliography

Papers referenced on the course website.

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Date of update January 15, 2017

Université Grenoble Alpes