What Makes Computer Vision Hard?
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A short introduction to state-of-the-art Computer Vision techniques and the own work of Anna Volokitin. A talk on the occasion of Geek Girls Carrot's meetup #9 in Zurich.
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What Makes Computer Vision Hard?
- 1. What Makes Computer Vision Hard? Anna Volokitin voanna@vision.ee.ethz.ch Geek Girls Carrots Zürich 23 January 2018 1
- 7. Ideally, we want to automatically answer any question about a photo or video. 7
- 8. e.g. why is he smiling? http://i.dailymail.co.uk/i/pix/2012/12/20/article-2250728-1696C1CD000005DC-817_964x628.jpg8
- 9. Returning to the basic task of recognition — is it really that hard? 9
- 10. Block world • We can write rules for recognising objects if we have • perfect lighting • simple shapes, • etc • Example: triangular prism • Step 1) Find image edges • Step 2) If any lines make a triangle, we found it. 10
- 11. What about the real world? 11
- 13. Illumination slide credit: S. Ullman 13
- 18. Rule based traditional programming won’t work - there are too many rules 18
- 19. Have to learn to recognise objects 19
- 20. Supervised Learning training set test set 20
- 21. Supervised Learning • A model is way to decide whether an image is a cat or a dog, based on some parameter • In this case, parameter is average colour • Training a model = adjusting parameter to make model more accurate is the image mainly this color ? Yes No CAT DOG 21
- 22. Training is the image mainly this color ? Yes No CAT DOG training set Model outputs that all cat images are DOG, so 50% accuracy22
- 23. Training is the image mainly this color ? Yes No CAT DOG training set Model outputs that all cat images are DOG, so 50% accuracy23
- 24. Training is the image mainly this color ? Yes No CAT DOG training set Model outputs that all cat images are DOG, so 50% accuracy24
- 25. Training is the image mainly this color ? Yes No CAT DOG training set Model outputs that all cat images are DOG, so 50% accuracy25
- 26. Training is the image mainly this color ? Yes No CAT DOG training set 100% accuracy, finally. Done training.26
- 27. Testing 100% test accuracy, great! 27 is the image mainly this color ? Yes No CAT DOG
- 28. But what if… FAIL! 28 is the image mainly this color ? Yes No CAT DOG
- 29. We need more parameters that we can tune to learn complicated rules 29
- 32. What do neural networks learn? 32
- 33. So what can we do with CNNs? 33
- 35. 35
- 36. Summary • To quantify meaning, CV breaks problems down into recognition, segmentation, etc. • Hard because of the huge variability of image appearance • Supervised learning to discover rules • Lots of cool applications with CNNs • recognition • segmentation • style transfer … 36