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Vision for Scene Understanding
Abstract
This manuscript covers my recent research on vision algorithms for scene understanding, articulated in 3 research axes: 3D Vision, Weakly supervised vision, and Vision and physics. At the core of the most recent works is weakly-supervised learning and physics-embodied vision, which address short comings of supervised learning that requires large amount of data. The use of more physically grounded algorithms appears evidently beneficial as both robots and humans naturally evolve in a 3D physical world. On the other hand, accounting for physics knowledge reflects important cue about lighting and weather conditions of the scene central in my work. Physics-informed machine learning is not only beneficial for increased interpretability but also to compensate labels and data scarcity- info:eu-repo/semantics/other
- Habilitation à diriger des recherches
- Computer vision
- Scene understanding
- Weak supervision
- Physics informed machine learning
- 3D scene understanding
- 3D scene reconstruction
- Computer vision and image understanding
- Autonomous driving
- Semantic scene understanding
- Reinforcement learning
- Machine learning
- Deep learning
- Vision par ordinateur
- Apprentissage machine
- [INFO]Computer Science [cs]
- [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]