Fractal Calibration for long-tailed object detection

Konstantinos Panagiotis Alexandridis, Ismail Elezi, Jiankang Deng, Anh Nguyen, Shan Luo

Abstract:

Real-world datasets follow an imbalanced distribution,
which poses significant challenges in rare-category object
detection. Recent studies tackle this problem by developing
re-weighting and re-sampling methods, that utilise the class
frequencies of the dataset. However, these techniques focus
solely on the frequency statistics and ignore the distribution
of the classes in image space, missing important information. In contrast to them, we propose FRActal CALibration
(FRACAL): a novel post-calibration method for long-tailed
object detection. FRACAL devises a logit adjustment
method that utilises the fractal dimension to estimate how
uniformly classes are distributed in image space. During
inference, it uses the fractal dimension to inversely downweight the probabilities of uniformly spaced class predictions achieving balance in two axes: between frequent and
rare categories, and between uniformly spaced and sparsely
spaced classes. FRACAL is a post-processing method and it
does not require any training, also it can be combined with
many off-the-shelf models such as one-stage sigmoid detectors and two-stage instance segmentation models. FRACAL
boosts the rare class performance by up to 8.6% and surpasses all previous methods on LVIS dataset, while showing good generalisation to other datasets such as COCO,
V3Det and OpenImages. We provide the code at https:
//github.com/kostas1515/FRACAL.