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2018 | OriginalPaper | Buchkapitel

Acquisition of Localization Confidence for Accurate Object Detection

verfasst von : Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS. In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective. Extensive experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well as its compatibility with and adaptivity to several state-of-the-art object detectors.

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Vorheriges Kapitel The Contextual Loss for Image Transformation with Non-aligned Data

Nächstes Kapitel Deep Model-Based 6D Pose Refinement in RGB

We prefer Precise RoI-Pooling layer to RoI-Align layer [9] as Precise RoI-Pooling layer is continuously differentiable w.r.t. the coordinates while RoI-Align is not.

The code is released at: https://github.com/vacancy/PreciseRoIPooling.

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Titel: Acquisition of Localization Confidence for Accurate Object Detection
verfasst von: Borui Jiang
Ruixuan Luo
Jiayuan Mao
Tete Xiao
Yuning Jiang
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01263-2

Electronic ISBN: 978-3-030-01264-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01264-9_48

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