Exploring Models and Data for Image Question Answering

NeurIPS 2015  ·  Mengye Ren, Ryan Kiros, Richard Zemel ·

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.

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Datasets


Introduced in the Paper:

COCO-QA

Used in the Paper:

COCO COCO Captions DAQUAR SUTD-TrafficQA
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Question Answering SUTD-TrafficQA VIS+LST 1/4 29.91 # 4
1/2 54.25 # 4

Methods


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