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Robust Object Detection With Inaccurate Bounding Boxes
[article]
2022
arXiv
pre-print
Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data. In this work, we aim to address the challenge of learning robust object detectors with inaccurate bounding boxes. Inspired by the fact that
arXiv:2207.09697v1
fatcat:c35gtdmkvvh5de2if64uagdlqe