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Found 6 papers, 6 papers with code
PatchDCT: Patch Refinement for High Quality Instance Segmentation
To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework.
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
Ranked #14 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation
Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity.
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.
Ranked #47 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall.
