Image Question Answering: A Visual Semantic Embedding Model and a New Dataset. (arXiv:1505.02074v1 [cs.LG])

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 recurrent neural networks and visual semantic embeddings without intermediate stages such as object detection and image segmentation. Our model performs 1.8 times better than the recently published results on the same dataset. Another main contribution is an automatic question generation algorithm that converts the currently available image description dataset into QA form, resulting in a 10 times bigger dataset with more evenly distributed answers.

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