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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Learning, cs.LG,Computer Science, Neural and Evolutionary Computing, cs.NE
Abstract:
Together with the development of more accurate methods in Computer Vision and
Natural Language Understanding, holistic architectures that answer on questions
about the content of real-world images have emerged. In this tutorial, we build
a neural-based approach to answer questions about images. We base our tutorial
on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the
models that we present here can achieve a competitive performance on both
datasets, in fact, they are among the best methods that use a combination of
LSTM with a global, full frame CNN representation of an image. We hope that
after reading this tutorial, the reader will be able to use Deep Learning
frameworks, such as Keras and introduced Kraino, to build various architectures
that will lead to a further performance improvement on this challenging task.
