Object Recognition

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Credits
4
Types
Elective
Requirements
This subject has not requirements
Department
UB;CS
In this course we will analyze the paradigm of automatic object recognition from a Computer Vision and Machine Learning points of view. We will review past and recent challenges in object recognition, such as multi-modal, multi-part, multi-scale, multi-view, multi-class, multi-label, and large scale object recognition, including recent deep learning architectures. We will also review current trends for a particular and complex kind of objects: ‘people in visual data'. We will deal with the problem of human pose recovery and automatic behavior analysis, describing potential applications as well as future lines of research in the field.

Teachers

Person in charge

  • Sergio Escalera Guerrero ( )

Others

  • Meysam Madadi ( )

Weekly hours

Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
0

Competences

Generic Technical Competences

Generic

  • CG2 - Capability to lead, plan and supervise multidisciplinary teams.

Technical Competences of each Specialization

Academic

  • CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.
  • CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA14 - Capability to understand the advanced techniques of Vision, Perception and Robotics, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Transversal Competences

Appropiate attitude towards work

  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Objectives

  1. Introduction to object and human recognition
    Multi-modal object recognition
    Multi-part object recognition
    Multi-scale object recognition
    Multi-view object recognition
    Multi-class object recognition
    Multi-label object recognition
    Multi-ple data: deep-learning for large scale object recognition
    Object Recognition in context: scene understanding and grammars
    Human Pose Recovery
    Human Behavior Analysis
    Related competences: CT5, CEA13, CEA14, CEA3, CEA4, CEA6, CB7, CEA8, CEP3, CEP6, CEP8, CG2,

Contents

  1. Introduction to object and human recognition
  2. Convolutional neural networks
  3. Recurrent Neural Networks in Vision
  4. Object detection and segmentation
  5. Human pose estimation
  6. Human Behavior
  7. Transformers / self-attention in Vision
  8. Graph Neural Networks in Vision

Activities

Activity Evaluation act


Paper presentation


Objectives: 1
Week: 10
Theory
1.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
5h

Paper presentation 2



Week: 14
Theory
1.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
5h

Exam



Week: 15 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h

Laboratory 1



Week: 2
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
4h

Laboratory 2



Week: 5
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
4h

Laboratory 3



Week: 8
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
4h

Laboratory 4



Week: 12
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
4h

Theoretical class



Theory
22.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Practical sessions



Theory
0h
Problems
0h
Laboratory
15h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

T – Each week it will be a 1.5h theoretical topic exposition class.
P – Each week it will be a 1h practical session.
The rest of the course are devoted to autonomous lectures, programming, and studying.

Evaluation methodology

The course will follow a continuous evaluation consisting in four practical reports (PR) and two in-class presentations (PS). At the end of the course a test exam will be performed (TS). The final score (FS) will be computed as follows:
FS = 0.5 * PR + 0.3 * PS + 0.2 * TS
A minimum score of 3 over 10 points is required for each part PR, PS, and TS in order to compute the final score FS.

Bibliography

Basic:

Complementary: