Towards Few-Annotation Learning in Computer Vision: Application to Image
  Classification and Object Detection tasks
Quentin Bouniot
In this thesis, we develop theoretical, algorithmic and experimental
contributions for Machine Learning with limited labels, and more specifically
for the tasks of Image Classification and Object Detection in Computer Vision.
In a first contribution, we are interested in bridging the gap between theory
and practice for popular Meta-Learning algorithms used in Few-Shot
Classification. We make connections to Multi-Task Representation Learning,
which benefits from solid theoretical foundations, to verify the best
conditions for a more efficient meta-learning. Then, to leverage unlabeled data
when training object detectors based on the Transformer architecture, we
propose both an unsupervised pretraining and a semi-supervised learning method
in two other separate contributions. For pretraining, we improve Contrastive
Learning for object detectors by introducing the localization information.
Finally, our semi-supervised method is the first tailored to transformer-based
detectors.