ROB313 - Deep Learning in Computer Vision

Programming 2024-2025

Professors:

Gianni FRANCHI
Vicky KALOGEITON
Andrei BURSUC

Teaching Assistants:

Marwane HARIAT
Firas GABETNI
Rémi KAZMIERCZAK

Assessment Method: Lab reports + written exam

This course presents a broad overview of recent computer vision methods based on machine learning.

Schedule	Session Description	Instructor	Resources	Room
Friday 29/11/2024 9:00–12:15	Introduction to Deep Learning + Semantic segmentation	Gianni FRANCHI	Lecture slides: Introduction to Deep Learning Lecture slides: Semantic segmentation	BEM-1021004
Friday 06/12/2024 9:00–12:15	Traditional Tracking + GAN Optional Homework (bonus 2 points): Implement a Conditional generative network or VQ-VAE on Fashion MNIST dataset	Marwane HARIAT	Lecture slides: Classical Tracking Lecture slides: Generative Adversarial Networks	BEM-1021004
Friday 13/12/2024 9:00–12:15	VAE, Diffusion Models	Vicky KALOGEITON	Lecture slides: VAE, Diffusion Models	BEM-1021004
Friday 20/12/2024 9:00–12:15	TP Deep Learning : Semantic Segmentation Optional Homework (bonus 2 points)	Gianni FRANCHI + Rémi KAZMIERCZAK + Marwane HARIAT	Lecture slides: Uncertainty Quantification link Optional Homework Uncertainty	BEM-1021004
Friday 10/01/2025 9:00–12:15	Tracking by detection Explainable AI Optional Homework (bonus 2 points)	Gianni FRANCHI + Rémi KAZMIERCZAK	Lecture slides: XAI Lecture slides: Tracking by detection Optional Homework Explainable AI	BEM-1021004
Friday 17/01/2025 9:00–12:15	Self-Supervised Learning	Gianni FRANCHI + Andrei BURSUC	Lecture slides: transfer learning Lecture slides: Visual Foundation Models	BEM-1021004
Friday 24/01/2025 9:00–12:15	EXAM	Gianni Franchi	EXAM 2023-2024	ENSTA R111