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Efficient Featurized Image Pyramid Network for Single Shot Detector
Tianjin Univ, Peoples R China.
Tianjin Univ, Peoples R China.
Incept Inst Artificial Intelligence, U Arab Emirates.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Incept Inst Artificial Intelligence, U Arab Emirates.
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2019 (English)In: 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), Long Beach, CA, JUN 16-20, 2019, IEEE , 2019, p. 7328-7336Conference paper, Published paper (Refereed)
Abstract [en]

Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed. However, while promising results have been achieved by these detectors on standard-sized objects, their performance on small objects is far from satisfactory. To detect very small/large objects, classical pyramid representation can be exploited, where an image pyramid is used to build afeature pyramid (featurized image pyramid), enabling detection across a range of scales. Existing single-stage detectors avoid such afeaturized image pyramid representation due to its memory and time complexity. In this paper we introduce a light-weight architecture to efficiently produce featurized image pyramid in a single-stage detection framework. The resulting multi-scale features are then injected into the prediction layers of the detector using an attention module. The performance of our detector is validated on two benchmarks: PASCAL VOC and MS COCO. For a 300 x 300 input, our detector operates at 111 frames per second (FPS) on a Titan X GPU, providing state-of-the-art detection accuracy on PASCAL VOC 2007 testset. On the MS COCO testset, our detector achieves state-of-the-art results surpassing all existing single-stage methods in the case of single-scale inference.

Place, publisher, year, edition, pages
IEEE , 2019. p. 7328-7336
Series
IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-168115DOI: 10.1109/CVPR.2019.00751ISI: 000542649300080ISBN: 978-1-7281-3293-8 (print)OAI: oai:DiVA.org:liu-168115DiVA, id: diva2:1458515
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Note

Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61632018]

Available from: 2020-08-17 Created: 2020-08-17 Last updated: 2020-08-17

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Khan, Fahad Shahbaz

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CiteExportLink to record
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  • apa
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Output format
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