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Pixels to Graphs by Associative Embedding
Alejandro Newell · Jia Deng

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #87 #None

Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph definition. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and demonstrate state-of-the-art performance on the challenging task of scene graph generation.

Author Information

Alejandro Newell (University of Michigan)
Jia Deng (University of Michigan)

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