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• W3113016743 abstract "This work presents the first convolutional neural network that learns an image-to-graph translation task without needing external supervision. Obtaining graph representations of image content, where objects are represented as nodes and their relationships as edges, is an important task in scene understanding. Current approaches follow a fully-supervised approach thereby requiring meticulous annotations. To overcome this, we are the first to present a self-supervised approach based on a fully-differentiable auto-encoder in which the bottleneck encodes the graph's nodes and edges. This self-supervised approach can currently encode simple line drawings into graphs and obtains comparable results to a fully-supervised baseline in terms of F1 score on triplet matching. Besides these promising results, we provide several directions for future research on how our approach can be extended to cover more complex imagery." @default.
• W3113016743 created "2020-12-21" @default.
• W3113016743 creator A5024010116 @default.
• W3113016743 creator A5037248049 @default.
• W3113016743 date "2020-12-10" @default.
• W3113016743 modified "2023-09-27" @default.
• W3113016743 title "Image-Graph-Image Translation via Auto-Encoding" @default.
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• W3113016743 hasPublicationYear "2020" @default.
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