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• W2953177298 abstract "Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deep-learning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmetry-labels from more than 400 human observers. We employ novel methods to convert discrete human labels into symmetry heatmaps, capture symmetry densely in an image and quantitatively evaluate Sym-NET against multiple existing computer vision algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO photos, Sym-NET significantly outperforms all other competitors. Beyond mathematically well-defined symmetries on a plane, Sym-NET demonstrates abilities to identify viewpoint-varied 3D symmetries, partially occluded symmetrical objects, and symmetries at a semantic level." @default.
• W2953177298 created "2019-06-27" @default.
• W2953177298 creator A5039386269 @default.
• W2953177298 creator A5065450968 @default.
• W2953177298 date "2017-04-11" @default.
• W2953177298 modified "2023-09-24" @default.
• W2953177298 title "Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild" @default.
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• W2953177298 doi "https://doi.org/10.48550/arxiv.1704.03568" @default.
• W2953177298 hasPublicationYear "2017" @default.
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