2021
DOI: 10.1002/int.22513
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Rotational multipyramid network with bounding‐box transformation for object detection

Abstract: The study proposes a rotational multipyramid network (RoMP Net) with bounding‐box transformation for object detection. The RoMP Net is a single‐stage object detection neural network featuring three characteristics. First, the network uses a rotational bounding box to minimize the effect of background images when extracting features of objects. Bounding‐box transformation was proposed to compensate for the limitation of the rotational bounding boxes, which have relatively low prediction accuracy for objects wit… Show more

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Cited by 15 publications
(14 citation statements)
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“…Object detection and classification are widely adopted in computer vision tasks including image segmentation, 13 event detection, 14 object tracking, 15 and so on. To deploy the deep learning inference models, fitting the resource constraints is one of the most challenging issues for edge‐end embedded devices 16,17 .…”
Section: Background and Related Workmentioning
confidence: 99%
“…Object detection and classification are widely adopted in computer vision tasks including image segmentation, 13 event detection, 14 object tracking, 15 and so on. To deploy the deep learning inference models, fitting the resource constraints is one of the most challenging issues for edge‐end embedded devices 16,17 .…”
Section: Background and Related Workmentioning
confidence: 99%
“…as it cannot be stored in memory and much less invert it for models with large parameters. For a model with parameters 2 space to calculate the Hessian. For models with a small number of parameters, the matrix can be pre-computed and stored explicitly, such that continuous requests for unlearning only involve a simple matrix-vector multiplication.…”
Section: Calculating Indirectly Hessianmentioning
confidence: 99%
“…The application of machine learning as a service has shown superior performance in various applications over diverse scenarios 1,2 . User data is employed in data analysis for various tasks such as disease diagnosis, personalized recommendations, 3 and credit scoring.…”
Section: Introductionmentioning
confidence: 99%
“…Considering the excellent performance of deep learning technologies, 30,31 the deep learning is utilized to capture forgery traces. Relying on the deep analysis of forgery methods, different traces have been utilized for forgery localization, such as noise level, 3,5,32,33 unnatural boundaries, 34 contrast/brightness inconsistencies, [35][36][37] pixels periodic correlation caused by interpolation, 1,3,19 and so on.…”
Section: Introductionmentioning
confidence: 99%