Rethinking Intersection Over Union for Small Object Detection in
  Few-Shot Regime
Pierre Le Jeune Anissa Mokraoui
In Few-Shot Object Detection (FSOD), detecting small objects is extremely
difficult. The limited supervision cripples the localization capabilities of
the models and a few pixels shift can dramatically reduce the Intersection over
Union (IoU) between the ground truth and predicted boxes for small objects. To
this end, we propose Scale-adaptive Intersection over Union (SIoU), a novel box
similarity measure. SIoU changes with the objects' size, it is more lenient
with small object shifts. We conducted a user study and SIoU better aligns than
IoU with human judgment. Employing SIoU as an evaluation criterion helps to
build more user-oriented models. SIoU can also be used as a loss function to
prioritize small objects during training, outperforming existing loss
functions. SIoU improves small object detection in the non-few-shot regime, but
this setting is unrealistic in the industry as annotated detection datasets are
often too expensive to acquire. Hence, our experiments mainly focus on the
few-shot regime to demonstrate the superiority and versatility of SIoU loss.
SIoU improves significantly FSOD performance on small objects in both natural
(Pascal VOC and COCO datasets) and aerial images (DOTA and DIOR). In aerial
imagery, small objects are critical and SIoU loss achieves new state-of-the-art
FSOD on DOTA and DIOR.