[Seminar] Few-Shot Object Detection in Aerial Images

The next LIVIA seminar will be held on Wednesday, September 28 at 12h00 in hybrid mode.

Title: Few-Shot Object Detection in Aerial Images
by Pierre Le Jeune, Ph.D. candidate at the L2TI, University Sorbonne, Paris

Abstract: Object Detection is a challenging task in computer vision. Recently, deep learning-based methods overcame classical algorithms both in terms of quality and speed. However, deep learning requires large annotated training sets to achieve such performance. Few-Shot Learning (FSL) aims to overcome this shortcoming by learning more efficiently on scarce data. While FSL was vastly explored in the literature, Few-Shot Object Detection (FSOD) only became a topic of interest very recently. Most authors develop and benchmark their methods on natural images and nothing guarantees the transfer of their performance on other kinds of images. This work focuses on applying FSOD to aerial images. First, we review the definition of FSOD and several existing methods to address this task. A performance analysis is done on aerial and natural images to understand the challenges of using such methods on aerial images. In light of this analysis, we propose a novel attention mechanism. It specifically targets small objects which appear to be extremely difficult to detect in the few-shot regime. Finally, we question the relevance of Intersection over Union (IoU) as a criterion for box similarity and suggest a scale-dependent version: Scaled-IoU which agrees better with human perception.

Bio: Pierre LE JEUNE is a PhD student at L2TI laboratory, University Sorbonne Paris Nord while working at COSE company. He received the M.Sc. degree in Mathematical Modelling and Computation from Danish Technical University (Copenhagen) and the M.Sc. in engineering from Centrale Nantes. His current research interests include Few-Shot Learning, Computer Vision and Deep Learning

* In person: ETS-LIVIA, room A-3600.