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Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics.
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Adam: A method for stochastic optimization
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A high resolution optical satellite image dataset for ship recognition and some new baselines
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Automatic differentiation in pytorch
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Decoupled weight decay regularization
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Polarmask: Single shot instance segmentation with polar representation
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Arbitrary-oriented object detection with circular smooth label
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Learning roi transformer for oriented object detection in aerial images
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Pointpillars: Fast encoders for object detection from point clouds
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Generalized intersection over union: A metric and a loss for bounding box regression
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Iou loss for 2d/3d object detection
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Arbitrary-oriented object detection in remote sensing images based on polar coordinates
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Swin transformer: Hierarchical vision transformer using shifted windows
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Oriented r-cnn for object detection
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Oriented object detection in aerial images with box boundary-aware vectors
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Polardet: A fast, more precise detector for rotated target in aerial images
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On the arbitrary-oriented object detection: Classification based approaches revisited
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Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing
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Mmrotate: A rotated object detection benchmark using pytorch
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