activity
20182022
most citedLearning Modulated Loss for Rotated Object Detection

63 citations · 64 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV2022

Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization

Xue Yang, Gefan Zhang, Xiaojiang Yang +5

Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated…

cs.CV20211 cited

RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection

Wen Qian, Xue Yang, Silong Peng +2

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degen…

cs.CV2021

Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss

Qi Ming, Lingjuan Miao, Zhiqiang Zhou +2

Arbitrary-oriented objects exist widely in natural scenes, and thus the oriented object detection has received extensive attention in recent years. The mainstream rotation detector…

cs.CV2020

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

Xue Yang, Liping Hou, Yue Zhou +2

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…

cs.CV201963 cited

Learning Modulated Loss for Rotated Object Detection

Wen Qian, Xue Yang, Silong Peng +2

Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss…

cs.CV2019

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Xue Yang, Junchi Yan, Ziming Feng +1

Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progre…