5 citations · 5 across the 4 of their papers we have counts for
4 papers · 1 filter
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 Classifica…
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
Quentin Bouniot, Angélique Loesch, Romaric Audigier +1
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much mor…
Proposal-Contrastive Pretraining for Object Detection from Fewer Data
Quentin Bouniot, Romaric Audigier, Angélique Loesch +1
The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detecti…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…