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Poster
Exploring Models and Data for Image Question Answering
Mengye Ren · Jamie Kiros · Richard Zemel

Mon Dec 04:00 PM -- 08:59 PM PST @ 210 C #8 #None
in Poster Session »

This work aims to address the problem of image-based question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embeddings, without intermediate stages such as object detection and image segmentation, to predict answers to simple questions about images. Our model performs 1.8 times better than the only published results on an existing image QA dataset. We also present a question generation algorithm that converts image descriptions, which are widely available, into QA form. We used this algorithm to produce an order-of-magnitude larger dataset, with more evenly distributed answers. A suite of baseline results on this new dataset are also presented.

Author Information

Mengye Ren (University of Toronto)
Jamie Kiros (University of Toronto)
Richard Zemel (University of Toronto)

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