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Tackling Challenges in Computer Vision

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Tackling Challenges in Computer Vision

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Augustin Marty, CEO @deepomatic, discussed computer visions' progress thanks to deep learning at the 2016 Hello Tomorrow Summit. He put forward a solution to tackle the challenges in computer vision, making AI for every company.

Augustin Marty, CEO @deepomatic, discussed computer visions' progress thanks to deep learning at the 2016 Hello Tomorrow Summit. He put forward a solution to tackle the challenges in computer vision, making AI for every company.

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Tackling Challenges in Computer Vision

  1. 1. In the past few years we have been witnessing incredible progress in the field of computer vision, mainly due to deep learning. Tackling challenges in computer vision Augustin Marty CEO Deepomatic
  2. 2. Deep learning changed so much in solving image related questions. You need to feed your model with examples. Give your model images, thousands of images so that it learns to differentiate. Imagenet: Iconic challenge in the world of computer vision –with thousands of categories: all the images must be placed by algorithms into the correct category. Democratisation of image recognition – error rate dropped from 26% to 3% today. 5% is the error rate of a human. DEEP LEARNING IS THE NEW PARADIGM
  3. 3. The deep learning progress was also made possible because the tech giants started to massively investing in it, developing bigger models (with more layers and millions of parameters) . This is a heavy process GoogleNet (2014) 22 LAYERS ResNet (2015) 152 LAYERS
  4. 4. With bigger and more complex models and algorithms there is a need for great computing power. Here NVIDIA’s CEO, Jen-Hsun Huang, is presenting and Open AI supercomputer to Elon Musk. This is a relatively small box, aligned with GPU processing calculations amazingly fast. This type of supercomputer is now affordable and accessible, especially since it is in the Cloud. This progress and computing power has led to interesting applications in image recognition: OPEN-AI SUPERCOMPUTER
  5. 5. google show and tell : Google’s image captioning model. The model is able to describe the scene with a collection of verbs, adjectives – like a human does. Google’s Show and Tell
  6. 6. Style transfer is another example: Deep learning algorithms can understand style of a painting and reproduce it. Here it understands Van Gogh’s expressionist painting and - coupled with a picture of houses along a river - reproduces the style, creating a new picture. Style Transfer
  7. 7. Video Coloration: Artificial intelligence colours the video turning the black and white video into a coloured one. How was this achieved?: 1.Engineers took thousands of coloured videos and made them black and white. 2. They then trained the algorithm to understand the correlation between the B&W videos and the respective coloured ones. 3. Then the algorithm was able to colour new, initially B&W videos. Video Colouration
  8. 8. In specific industry problems those models don’t necessarily work. Here the following 3 images are taken from Microsoft’s image recognition platform online. Here an automotive part is mistaken for a “close up of a plane” - not quite! “Close up of a plane”
  9. 9. A terrorist is labeled as ‘man looking at the ocean' . Of course there's an ocean and a man but first of all he’s clearly looking away from the ocean. The machine doesn’t recognise the dark knife in his hands, and can’t properly identify the face because of the mask. “Man looking at the ocean”
  10. 10. a group of men standing on a dirt field : it’s not wrong: you have a group of men and yes it’s a dirt road. But we humans, understand the context of this picture better , unlike the algorithm: these group of men are fighters – they have weapons, they are fighters, and are probably engaging in an act of war All this goes to show that progress is undeniable but there is much to still do. When you want to apply these technologies to your industry or company specific challenges it might not work. You may think that therefore its not for you, that AI and computer vision doesn’t solve your need But… “Group of men standing on top of a dirt field”
  11. 11. Artificial Intelligence is for everyone, for every company. The following examples show industry specific problems that were solved thanks to computer visions AI IS FOR EVERY COMPANY
  12. 12. SADAKO TECHNOLOGIES- a Spanish firm- has developed a waste sorting device by combining robotics and computer vision. They are therefore able to automatically distinguish plastic from other waste on a conveyer belt. This can have incredibly promising applications for the future waste sorting systems and management,, and other cleaning applications CASE STUDY: SADAKO Waste Sorting
  13. 13. Regaind is a startup that is able to qualify the image aesthetics. Selecting best pictures (amongst thousands of pictures taken during a vacation) it creates photo albums automatically by analysing the quality of the picture. CASE STUDY: REGAIND Image Aesthetics
  14. 14. Coming back to the image of the fighters. Its possible, with todays’ technology, to develop weapon detection for images and in videos. This has great use and is of great importance to military and intelligence. CASE STUDY: SECURITY Weapon Detection
  15. 15. The three previous application have been developed by small companies. If they were able to do this than so can you, if you follow the right methodology There is a secret sauce to tackle image recognition challenges specific to your industry: THE SECRET AI SAUCE TO SOLVE YOUR PROBLEMS
  16. 16. First, you need a deep learning framework. These are available as they are open source. You just need an engineer to use them. A framework
  17. 17. Second ingredient: annotated images. These must be relevant to the task you are tackling The images differ for each use-case and problem – need to develop a dataset. ANNOTATED DATA
  18. 18. Annotators – human in the loop HUMANS-IN-THE-LOOP
  19. 19. Assemble the 3 ingredients; You trained an algorithm thanks to your dataset and framework. Your first algorithm is applied to never-before-seen images (it is never perfect at first). Your algorithm won’t give an answer on all new images provides: you need humans-in-the-loop to keep annotating those that weren’t (when the machine lacked confidence), completing the task. AI + annotators who complete the job and also keeps building the dataset, creating a better algorithm … Dataset Neural network models Humans in the loop TrainingAnnotation Calling humans when the model is not sure THE LOOP
  20. 20. This may all seem relatively simple but there’s a catch: you need to have a very good dataset for it to work well: a huge amount of perfectly annotated images Creating these datasets takes lot’s of time. BUT… THERE’S A CATCH
  21. 21. Here’s an example of furniture detection To develop and algorithm that detects furniture in images you need a dataset with boxes around every single item in the image. Consequently, you need to do this manually at first, making sure the boxes are perfectly around the item, and that no object is missed. This takes 10 minutes for 1 image! Sadako Technology, mentioned earlier, needed to do this to train their technology: they put millions of boxes around plastic bottles to create their dataset. FURNITURE DETECTION (10 min)
  22. 22. Some tasks are even more time consuming. If you want to develop algorithms for robotics (automated cars, robots, drones etc.) – they need to understand their entire environment. So in this case, to train algorithms you need to determine what each pixel represents in the image. This segmentation task takes over an hour URBAN SEGMENTATIO N (70min)
  23. 23. The real bottleneck is now the dataset creation DATA IS AI BOTTLENECK
  24. 24. Good dataset creation is crucial to speed up the pace of AI progress LACK OF DATA IS SLOWING DOWN AI EXPANSION
  25. 25. To make datasets today there are 2 ways of doing it for now: 1) done internally by data scientists 2) Use crowdsourcing, such s Amazons Mechanical Turk – this isn’t too bad – but is time consuming and you need to do many quality reviews and check to ensure satisfactory results Every data scientist has, at least once, thrown out a dataset due to its poor quality. AMAZON MECHANICAL TURK Time consuming, poor quality DO IT INTERNALLY Make your data scientists want to quit or Solutions
  26. 26. Real need for Industrialising the dataset creation process is the true solution to move forward to solve image related challenges for each company. There are a few elements that may help the industrialisation and democratisation of the dataset creation: INDUSTRIALISING THE ANNOTATION PROCESS
  27. 27. 1. Improve the UX of annotation tools We need to have a dedicated software: today there is no software to produce datasets. Its crazy to think that each company develops their own small software. A big leap in productivity can be achieved by simply improving the design and the annotation experience
  28. 28. Second element to increase pace fo AI production is to work on active learning. You don’t want to annotate millions of images. Active learning is science that helps select the most informative image to build AI with as few images as possible 2. Active Learning & HITL
  29. 29. Improve software with machine learning: if software knows what you’re doing it can really improve the ease of the 3. Improve tools with AI
  30. 30. We intend to reduce the time from 70 to 5 minutes increasing the productivity by 10x We intend to reduce the time from 70 to 5 minutes in 10 months
  31. 31. Here machine learning helps software to annotate images. This video shows that if you are looking for a face the box will automatically adjust around the head. The same goes for when annotating objects pixel-wise – speeding up the completion of the task.
  32. 32. AI really is for everyone and can solve any companies challenges. Algorithms are becoming commodities and datasets are the bottle neck of AI. Democratising and industrialising the process of dataset creation will allow for all of us, all companies to move forward with their AI. Applications and goals. THANK YOU

Notas del editor

  • In the past few years we have been witnessing incredible progress in the field of computer vision, mainly due to deep learning.
  • Deep learning changed so much in solving image related questions.
    You need to feed your model with examples. Give your model images, thousands of images so that it learns to differentiate. Imagenet: Iconic challenge in the world of computer vision –with thousands of categories: all the images must be placed by algorithms into the correct category.
    Democratisation of image recognition – error rate dropped from 26% to 3% today. 5% is the error rate of a human.

  • The deep learning progress was also made possible because the tech giants started to massively investing in it, developing bigger models (with more layers and millions of parameters) .
    This is a heavy process

  • With bigger and more complex models and algorithms there is a need for great computing power. Here NVIDIA’s CEO, Jen-Hsun Huang, is presenting and Open AI supercomputer to Elon Musk.
    This is a relatively small box, aligned with GPU processing calculations amazingly fast.
    This type of supercomputer is now affordable and accessible, especially since it is in the Cloud.
    This progress and computing power has led to interesting applications in image recognition:
  • google show and tell : Google’s image captioning model. The model is able to describe the scene with a collection of verbs, adjectives – like a human does.
  • Style transfer is another example:
    Deep learning algorithms can understand style of a painting and reproduce it. Here it understands Van Gogh’s expressionist painting and - coupled with a picture of houses along a river - reproduces the style, creating a new picture.
  • Video Coloration:
    Artificial intelligence colours the video turning the black and white video into a coloured one.
    How was this achieved?:
    1.Engineers took thousands of coloured videos and made them black and white. 2. They then trained the algorithm to understand the correlation between the B&W videos and the respective coloured ones.
    3. Then the algorithm was able to colour new, initially B&W videos.

  • In specific industry problems those models don’t necessarily work. Here the following 3 images are taken from Microsoft’s image recognition platform online. Here an automotive part is mistaken for a “close up of a plane” - not quite!
  • A terrorist is labeled as ‘man looking at the ocean' . Of course there's an ocean and a man but first of all he’s clearly looking away from the ocean. The machine doesn’t recognise the dark knife in his hands, and can’t properly identify the face because of the mask.
  • a group of men standing on a dirt field : it’s not wrong: you have a group of men and yes it’s a dirt road. But we humans, understand the context of this picture better , unlike the algorithm: these group of men are fighters – they have weapons, they are fighters, and are probably engaging in an act of war

    All this goes to show that progress is undeniable but there is much to still do.
    When you want to apply these technologies to your industry or company specific challenges it might not work. You may think that therefore its not for you, that AI and computer vision doesn’t solve your need

    But…
  • Artificial Intelligence is for everyone, for every company. The following examples show industry specific problems that were solved thanks to computer visions
  • SADAKO TECHNOLOGIES- a Spanish firm- has developed a waste sorting device by combining robotics and computer vision.
    They are therefore able to automatically distinguish plastic from other waste on a conveyer belt. This can have incredibly promising applications for the future waste sorting systems and management,, and other cleaning applications
  • Regaind is a startup that is able to qualify the image aesthetics. Selecting best pictures (amongst thousands of pictures taken during a vacation) it creates photo albums automatically by analysing the quality of the picture.
  • Coming back to the image of the fighters.
    Its possible, with todays’ technology, to develop weapon detection for images and in videos. This has great use and is of great importance to military and intelligence.
  • The three previous application have been developed by small companies. If they were able to do this than so can you, if you follow the right methodology
    There is a secret sauce to tackle image recognition challenges specific to your industry:
  • First, you need a deep learning framework. These are available as they are open source. You just need an engineer to use them.
  • Second ingredient: annotated images. These must be relevant to the task you are tackling
    The images differ for each use-case and problem – need to develop a dataset.
  • Annotators – human in the loop
  • Assemble the 3 ingredients;
    You trained an algorithm thanks to your dataset and framework.
    Your first algorithm is applied to never-before-seen images (it is never perfect at first). Your algorithm won’t give an answer on all new images provides: you need humans-in-the-loop to keep annotating those that weren’t (when the machine lacked confidence), completing the task. AI + annotators who complete the job and also keeps building the dataset, creating a better algorithm …
  • This may all seem relatively simple but there’s a catch: you need to have a very good dataset for it to work well: a huge amount of perfectly annotated images
    Creating these datasets takes lot’s of time.
  • Here’s an example of furniture detection
    To develop and algorithm that detects furniture in images you need a dataset with boxes around every single item in the image. Consequently, you need to do this manually at first, making sure the boxes are perfectly around the item, and that no object is missed. This takes 10 minutes for 1 image! Sadako Technology, mentioned earlier, needed to do this to train their technology: they put millions of boxes around plastic bottles to create their dataset.
  • Some tasks are even more time consuming. If you want to develop algorithms for robotics (automated cars, robots, drones etc.) – they need to understand their entire environment. So in this case, to train algorithms you need to determine what each pixel represents in the image. This segmentation task takes over an hour
  • The real bottleneck is now the dataset creation
  • Good dataset creation is crucial to speed up the pace of AI progress
  • To make datasets today there are 2 ways of doing it for now: 1) done internally by data scientists
    2) Use crowdsourcing, such s Amazons Mechanical Turk – this isn’t too bad – but is time consuming and you need to do many quality reviews and check to ensure satisfactory results

    Every data scientist has, at least once, thrown out a dataset due to its poor quality.
  • Real need for Industrialising the dataset creation process is the true solution to move forward to solve image related challenges for each company.
    There are a few elements that may help the industrialisation and democratisation of the dataset creation:
  • We need to have a dedicated software: today there is no software to produce datasets. Its crazy to think that each company develops their own small software.
    A big leap in productivity can be achieved by simply improving the design and the annotation experience
  • Second element to increase pace of AI production is to work on active learning.
    You don’t want to annotate millions of images. Active learning is science that helps select the most informative image to build AI with as few images as possible
  • Improve software with machine learning: if software knows what you’re doing it can really improve the ease of the task.
  • We intend to reduce the time from 70 to 5 minutes increasing the productivity by 10x
  • Here machine learning helps software to annotate images. This video shows that if you are looking for a face the box will automatically adjust around the head. The same goes for when annotating objects pixel-wise – speeding up the completion of the task.
  • AI really is for everyone and can solve any companies challenges. Algorithms are becoming commodities and datasets are the bottle neck of AI.
    Democratising and industrialising the process of dataset creation will allow for all of us, all companies to move forward with their AI. Applications and goals. THANK YOU

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