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Found 6 papers, 3 papers with code
Rethinking Boundary Discontinuity Problem for Oriented Object Detection
We decouple reversibility and joint-optim from single smoothing function into two distinct entities, which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed.
Gaussian Label Distribution Learning for Spherical Image Object Detection
Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance.
Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting
To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2.
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
Dense Scale Network for Crowd Counting
The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales.
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing
Accordingly, DR$^{2}$-Net consists of two components, \emph{i. e.,} linear mapping network and residual network, respectively.
