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Advanced Convolutional Neural Networks
Casing, HyperNets, DenseNets, Inception, and Xception are all available as
pretrained nets in both tf.keras.application and TF-Hub. The Keras application
(https://keras.io/applications) reports a nice summary of the performance
achieved on an ImageNet dataset and the depth of each network:
Figure 24: Performance summary, shown by Keras
In this section, we have discussed many CNN architectures. Next, we are going
to see how to answer questions about images by using CNNs.
Answering questions about images (VQA)
One of the nice things about neural networks is that different media types can
be combined together to provide a unified interpretation. For instance, Visual
Question Answering (VQA) combines image recognition and text natural language
processing. Training can use VQA (https://visualqa.org/), a dataset containing
open-ended questions about images. These questions require an understanding
of vision, language, and common knowledge to answer. The following images are
taken from a demo available at https://visualqa.org/.
Note the question at the top of the image, and the subsequent answers:
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