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.
Pour les étudiants du diplôme M2 Data Science
Basic knowledge of deep learning is required.
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme M2 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.
