6 Matching Annotations
- Aug 2022
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deephaven.io deephaven.io
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Preview: quick before and after
Check out the
Previewsection to see how much better the blog post images are when generated by DALL·E 2 for $45
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- Mar 2020
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victorzhou.com victorzhou.com
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Here’s a very simple example of how a VQA system might answer the question “what color is the triangle?”
- Look for shapes and colours using CNN.
- Understand the question type with NLP.
- Determine strength for each possible answer.
- Convert each answer strength to % probability
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Visual Question Answering (VQA): answering open-ended questions about images. VQA is interesting because it requires combining visual and language understanding.
Visual Question Answering (VQA) = visual + language understanding
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Most VQA models would use some kind of Recurrent Neural Network (RNN) to process the question input
- Most VQA will use RNN to process the question input
- Easier VQA datasets shall be fine with using BOW to transport vector input to a standard (feedforward) NN
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The standard approach to performing VQA looks something like this: Process the image. Process the question. Combine features from steps 1/2. Assign probabilities to each possible answer.
Approach to handle VQA problems:
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- Feb 2020
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github.com github.com
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Of the three primary color channels, red, green and blue, green contributes the most to luminosity.
Green colour vs red and blue (RGB)
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