Pixels to Graphs by Associative Embedding

Alejandro Newell; Jia Deng

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arXiv · 1706.07365

Pixels to Graphs by Associative Embedding

Alejandro Newell·Jia Deng

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Abstract

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.

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Alejandro Newell, Jia Deng. 2017-06-22. Pixels to Graphs by Associative Embedding. https://arxiv.org/abs/1706.07365

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