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Enseignement de Master - APM_5DA12_TP : Deep Learning for Computer Vision

Descriptif

The course focuses on various advanced topics in the field. Students will delve into areas such as few-shot learning and domain adaptation, exploring techniques that enable models to learn from limited labeled data and adapt to new domains. The course also covers advanced methods for image and video generation and editing, allowing students to gain insights into cutting-edge approaches for creating and manipulating visual content. Classical vision tasks, including object detection and human pose estimation, are extensively studied, providing students with a strong foundation in fundamental computer vision techniques. Additionally, the course delves into video understanding, equipping students with the necessary tools to extract meaningful information from video data. Lastly, students will explore the integration of vision with other sensors, delving into the fusion of visual information with data from other sensing modalities, opening up new possibilities for perception and analysis. The course will be composed of five lectures and two practical sessions.

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Master M1 - Data and Artificial Intelligence

Pour les étudiants du diplôme Echange international non diplomant

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS

Pour les étudiants du diplôme M2 DS - Data Science

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS

Pour les étudiants du diplôme Diplôme d'ingénieur

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS

Pour les étudiants du diplôme Master M2 - Data & Artificial Intelligence

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS
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