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Noisy Localization Annotation Refinement for Object Detection

Jiafeng MAO, Qing YU, Kiyoharu AIZAWA

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Summary :

Well annotated dataset is crucial to the training of object detectors. However, the production of finely annotated datasets for object detection tasks is extremely labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.9 pp.1478-1485
Publication Date
2021/09/01
Publicized
2021/05/25
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDP7026
Type of Manuscript
PAPER
Category
Image Recognition, Computer Vision

Authors

Jiafeng MAO
  The University of Tokyo
Qing YU
  The University of Tokyo
Kiyoharu AIZAWA
  The University of Tokyo

Keyword