Instance Shadow Detection with A Single-Stage Detector
- URL: http://arxiv.org/abs/2207.04614v1
- Date: Mon, 11 Jul 2022 04:15:42 GMT
- Title: Instance Shadow Detection with A Single-Stage Detector
- Authors: Tianyu Wang, Xiaowei Hu, Pheng-Ann Heng, Chi-Wing Fu
- Abstract summary: We first compile a new dataset with the masks for shadow instances, object instances, and shadow-object associations.
We then design an evaluation metric for quantitative evaluation of the performance of instance shadow detection.
We quantitatively and qualitatively evaluate our method on the benchmark dataset of instance shadow detection and show the applicability of our method on light direction estimation and photo editing.
- Score: 126.73011063999695
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper formulates a new problem, instance shadow detection, which aims to
detect shadow instance and the associated object instance that cast each shadow
in the input image. To approach this task, we first compile a new dataset with
the masks for shadow instances, object instances, and shadow-object
associations. We then design an evaluation metric for quantitative evaluation
of the performance of instance shadow detection. Further, we design a
single-stage detector to perform instance shadow detection in an end-to-end
manner, where the bidirectional relation learning module and the deformable
maskIoU head are proposed in the detector to directly learn the relation
between shadow instances and object instances and to improve the accuracy of
the predicted masks. Finally, we quantitatively and qualitatively evaluate our
method on the benchmark dataset of instance shadow detection and show the
applicability of our method on light direction estimation and photo editing.
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