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• W3174389852 abstract "Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc. Differing from the dominant regression-based approaches for orientation estimation, this paper explores a relatively less-studied methodology based on classification. The hope is to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors. We propose new techniques to push its frontier in two aspects: i) new encoding mechanism: the design of two Densely Coded Labels (DCL) for angle classification, to replace the Sparsely Coded Label (SCL) in existing classification-based detectors, leading to three times training speed increase as empirically observed across benchmarks, further with notable improvement in detection accuracy; ii) loss re-weighting: we propose Angle Distance and Aspect Ratio Sensitive Weighting (ADARSW), which improves the detection accuracy especially for square-like objects, by making DCL-based detectors sensitive to angular distance and object’s aspect ratio. Extensive experiments and visual analysis on large-scale public datasets for aerial images i.e. DOTA, UCAS-AOD, HRSC2016, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach. The source code is available at $color{Red}{text{DCL}}$ and is also integrated in our open source rotation detection benchmark:$color{Red}{text{RotationDetection}}$." @default.
• W3174389852 created "2021-07-05" @default.
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• W3174389852 date "2021-06-01" @default.
• W3174389852 modified "2023-10-11" @default.
• W3174389852 title "Dense Label Encoding for Boundary Discontinuity Free Rotation Detection" @default.
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• W3174389852 doi "https://doi.org/10.1109/cvpr46437.2021.01556" @default.
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