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Efficient Featurize...
Efficient Featurized Image Pyramid Network for Single Shot Detector
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- Pang, Yanwei (author)
- Tianjin Univ, Peoples R China
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- Wang, Tiancai (author)
- Tianjin Univ, Peoples R China
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- Anwer, Rao Muhammad (author)
- Incept Inst Artificial Intelligence, U Arab Emirates
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- Khan, Fahad Shahbaz, 1983- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Incept Inst Artificial Intelligence, U Arab Emirates
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- Shao, Ling (author)
- Incept Inst Artificial Intelligence, U Arab Emirates
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(creator_code:org_t)
- IEEE, 2019
- 2019
- English.
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In: 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), Long Beach, CA, JUN 16-20, 2019. - : IEEE. - 9781728132938 ; , s. 7328-7336
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publication and Content Type
- ref (subject category)
- kon (subject category)
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