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• W2921231843 abstract "Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent manner using a CNN-RNN architecture, allowing interactive correction via humans-in-the-loop. We propose a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN). Our model is trained end-to-end. It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved objects. We show that Curve-GCN outperforms all existing approaches in automatic mode, including the powerful PSP-DeepLab and is significantly more efficient in interactive mode than Polygon-RNN++. Our model runs at 29.3ms in automatic, and 2.6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++." @default.
• W2921231843 created "2019-03-22" @default.
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• W2921231843 date "2019-03-15" @default.
• W2921231843 modified "2023-09-29" @default.
• W2921231843 title "Fast Interactive Object Annotation with Curve-GCN" @default.
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• W2921231843 doi "https://doi.org/10.48550/arxiv.1903.06874" @default.
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