Teaching code

4S010679

Teacher

Vittorio Murino

Coordinator

Vittorio Murino

Credits

6

Also offered in courses:

• Computer Vision & Deep Learning of the course Master's degree in Artificial intelligence

Language

English

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

2nd semester dal Mar 2, 2026 al Jun 12, 2026.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

Computer vision (CV) issues are traditionally tackled by adopting machine learning methods. Recently, deep learning (DL) approaches showed to face CV applications (e.g., detection, classification, segmentation, tracking, etc.) in a most effective and efficient way, reaching performance never imagined before, even allowing to tackle new problems (e.g., image generation, style transfer, to cite a few). This course aims at describing how main CV topics are faced, and even solved, by DL approaches: It will address classical, yet significant and with broad applicability in real scenarios, CV topics, which are stil open issues, involving image and video analysis and recognition, as well as other multimodal data (3D, audio, etc.). The most significant and effective DL approaches will be detailed including, but not limited to, convolutional neural networks, autoencoder architectures, recurrent models, domain adaptation frameworks, while addressing practical problems usually met in real applications such as scarcity of annotated data (unsupervised, self-supervised, few/zero-shot learning), data augmentation and generation, robustness to adversarial attacks, and continual lifelong learning.

Examination methods

To pass the exam, students must demonstrate:
- to have understood the theoretical principles and algorithms underlying the Computer Vision & Deep Learning techniques described in class;
- to be able to present their arguments in a precise, organic and structured way, without digressions;
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.
The exam will consist in the development of a project, followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked on the course contents described in class.

Prerequisites and basic notions

The prerequisites for following the course consist of having acquired skills in Machine Learning, Pattern Recognition and Artificial Intelligence in general. Knowledge of issues related to image processing is also essential for understanding the course in question.

Program

The course intends to explain the modern methods for facing and solving Computational Vision problems. These methods basically consist in Deep Learning approaches applied to image and video processing.
In particular, the course will develop on monolithic topics related to specific open problems in Vision such as:
- Segmentation
- Object detection
- Object Recognition
- Image Classification
- Pose estimation
- Tracking
- Action & Activity Recognition
- Action localization
- 3D data processing
- Generative models
- Image retrieval
- Multimodal data analysis
- Vision and Language
- Domain adaptation and generalization
- Transfer Learning and representation learning
- Training with scarce, noisy, unbalanced data

Didactic methods

Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be on the computer in the computer room and will consist in the development of some of the algorithms developed in class.
The Laboratory lessons will be aimed at developing practical examples of some of the topics described in the Theory part of the course. The lessons will take place in a computer laboratory in Phyton language.

Learning assessment procedures

The exam will consist in the development of a project (2 people max, 3 people inexceptional cases, to be agreed with the teachers), followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked about the theoretical part of the course described in class, aimed at assessing the knowledge of the contents of the course.

Evaluation criteria

To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning, Deep Learning & Artificial Intelligence techniques described in class;
- be able to present one's arguments in a precise, organic and structured way, without digressions;
- knowing how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.

Criteria for the composition of the final grade

The final grade will be a combination of the project grade and the answers to the theory questions.

Exam language

Inglese