Computer Vision problematics in the industry by Despoina Ioannidou, Team Lead R&D @Meero
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During her talk, Despoina will present some problematics that they had to tackle while developing scalable computer vision applications at Meero.
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Computer Vision problematics in the industry by Despoina Ioannidou, Team Lead R&D @Meero
- 1. Computer Vision problematics in the industry: the Meero case Despoina Ioannidou 8/10/2019 Paris Women in Machine Learning and Data Science
- 2. Meero’s mission On-demand end-to-end photo shoots delivery Clients Client orders shoots & pays Meero 1 Meero matches it with suited photographers 2 Meero qualifies, trains & onboards photographers 0 Photographers shoot 3 Photographers upload DSLR photos & are paid by Meero 4 Photographs go through AI editing & human final touch 5 Client retrieves the media 6 Handling 2000+ shoots a week in 90+ countries
- 3. Where computer vision and DL come in: Scalability Upgrade a photo shoot to a professional look And make it automatic…………...
- 4. Automatic editing - the journey Summarize all the problems to enhance an image ➔ white balance ➔ exposure ➔ color enhancement ➔ sharpness ➔ contrast enhancement ➔ green-magenta tints ➔ noise ➔ compression artefacts ➔ blur ➔ dirt ➔ low point of view ➔ rotation ➔ tilt ➔ shadows attenuation ➔ flash reflections ➔ lens distorsions …. 1 round Test automatic software editors... 2 round 3 round
- 5. De-Bayer White balance RAW to 8 bits conversion Sharpening Blur Noise reduction
- 6. AI editing - the journey focus on the most salient issues: exposure & color enhancement, white balance Photographer’s shoot Expected result 1 round 2 round 3 round
- 7. Auto mode of photo editing software Photographer’s shoot Editor #1: yellow, underexposed AI editing - the journey 1 round 2 round 3 round
- 8. Auto mode of photo editing software Photographer’s shoot Editor #2: yellow & magenta, underexposed AI editing - the journey 1 round 2 round 3 round
- 9. Auto mode of photo editing software Photographer’s shoot Editor #3: too saturated, underexposed AI editing - the journey 1 round 2 round 3 round
- 10. second round feedbacks - Several bias per editor (green-magenta bias, underexposure, saturation, etc.) - Global settings are not sufficient to edit a photo with professional quality result - Image segmentation to apply locally tuned changes is not a solution, it takes quite a while for mitigated results - Our images are HUUUGE (Minimum 15Mpix, up to… 50Mpix) AI editing - the journey 1 round but... - We have a very wide dataset of pro-edited real estate images - We have no processing constraints for production 2 round 2 round 3 round
- 11. AI editing - the journey 1 round 3 round 2 round define the problematic - What can we build to allow meero work faster? - What does automatic retouching consists in? Case of automatic retouching: - Technical retouching: noise, artifacts, blur,... - Geometry retouching: rotation, tilt,... - Aesthetic retouching: colours, exposure,... break it down in smaller modules assess feasibility - What can be attained in a week, in a month, in a year? - What can be attained by development, what is research and what is r&d
- 12. AI editing - the journey 1 round round 3 round 2 our toolbox
- 13. Problematic to address Aesthetic improvement Photographer’s shoot Expected result
- 14. … based on M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, F. Durand Deep Bilateral Learning for Real-Time Image Enhancement - Siggraph 2017 AI editing - the journey 1 round 2 round 3 round Design our own conv-deconv deep network
- 15. and on... O. Ronneberg, P. Fischer, T. Brox, U-Net: convolutional networks for biomedical image segmentation - CoRR 2015 AI editing - the journey 1 round 2 round 3 round Design our own conv-deconv deep network
- 16. Auto mode of photo editing software Photographer’s shoot Editor #1: yellow, underexposed Reminder-other editors
- 17. Auto mode of photo editing software Photographer’s shoot Editor #2: yellow & magenta, underexposed Reminder-other editors
- 18. Auto mode of photo editing software Photographer’s shoot Editor #3: too saturated, underexposed Reminder-other editors
- 19. Here are the results... AI editing - the journey 1 round 2 round 3 round
- 20. AI editing - the aftermath Did we achieve our goal? Is the retouched image beautiful enough? - Experts opinion is guiding us but... - Research motivated by this question: Aesthetic metric learning: Can we teach a machine to learn to “understand” how aesthetically pleasing an image is? What features make a photo
- 21. Are AI edited images still photographs ? Editing changes the way you see the photos. It is like fake news ! AI will have biases that the conceiver will maybe not forecast Photo is an art - emotion is better than accuracy I want to feel the same atmosphere as during the shoot, editing is important The truth is the raw image from my camera The cultural background is key, images will look average Reality is in the eye of the observer
- 22. And it is only the beginning... Scene parsing and understanding Unified Perceptual Parsing for Scene Understanding, T Xiao, 2018
- 23. ...of a new love story It’s a new match ;) AI Camera born in 1956 born in 1827