Descriptif
This course explores advanced topics in computer vision, focusing on deep learning techniques and recent developments in the field. Through a combination of lectures and practical sessions, students will develop a comprehensive understanding of key research challenges in computer vision and the corresponding methodologies.
effectifs minimal / maximal:
/120Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme M2 DS - Data Science
Le rattrapage est autorisé (Max entre les deux notes)- Crédits ECTS acquis : 3 ECTS
Programme détaillé
Course Outline:
Week 1-2: Introduction to the main vision tasks
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Object detection and segmentation
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Depth estimation
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Human pose estimation
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Action recognition
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Practical sessions
Week 3-4: Self-Supervised Learning, Few-Shot Learning and Domain Adaptation
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Introduction to self-supervised learning
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Contrastive learning
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Few-shot learning: Problem formulation and techniques
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Domain adaptation: Theory and applications
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Recent advances in few-shot learning
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Practical session
Week 5: Recent Advances in Image and Video Generation
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Recap on Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models
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Conditional image synthesis
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Text-to-image generation
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Video generation and editing
Week 6-7: Deep Architectures for Point Cloud Perception
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Introduction to point cloud data
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PointNet and PointNet++
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Point cloud segmentation
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Object recognition in point clouds
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Practical session
Please note that the specific topics and content may be adjusted based on the needs and preferences of the students and the availability of resources.
