Toronto Annotation Suite

How our team helped speed up standard annotations for Computer Vision through human-in-the-loop AI models by developing new algorithms

We actively participated in developing a Toronto Annotation Platform - a web-based application that allows for larger, more efficient, and more accurate data annotation projects.

Project duration jun 2020 - dec 2020

The research group of the University of Toronto is working on tools to speed up standard annotations for computer vision. We’re a proud member of the research group, working closely with the leading expert in computer vision - prof. Sanja Fidler.

The Challenge

A modern approach to machine learning still heavily depends on annotated data, primarily connected through manual labor. It’s a time-consuming process prone to human error. We wanted to improve the process through human-in-the-loop AI models and new algorithms.

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    Artificial intelligence

    Artificial intelligence

    Use of machine learning algorithms with the inclusion of human interaction.
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    Preobrazba procesov

    High impact

    Potential to use the solution in many areas (computer vision, medicine, sports, fashion ...).
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    Mathematical modeling

    Mathematical algorithms developed on cutting edge AI technologies.
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    Agilni pristop

    International R&D

    We are actively cooperating with leading experts in the field of AI.

The Solution: Algorithms that assist manual annotators

Together with our partners, we have successfully developed algorithms that speed up the annotation process. Using recurrent and convolutional neural networks, the algorithms assist human annotators through both annotation creation and editing, while simultaneously learning to continuously improve from human assistance.

These state-of-the-art algorithms are fully integrated into the Toronto Annotation Platform, a web-based application that allows for larger, more efficient, and more accurate data annotation projects.

The result

Modern methods of machine learning depend heavily on extensive collections of annotated data, which to this day are still prepared with completely "manual" human work.

  • Freehand annotation

    Using the freehand tool, the user roughly marks the desired detail in the image.

    Preprosta človeška anotacija

  • Automatic enhancement

    The tool quickly and significantly improves annotation using RNN and CNN algorithms.

    Izboljšava natančnosti z umetno inteligenco

This solution outperforms the standard, manual annotation in both automatic (10% absolute and 16% relative improvement in mean Intersection over Union) and interactive modes (requiring 50% fewer clicks by annotators).

  • Frame annotation

    Using the rectangular selection tool, the user roughly marks the desired detail in the image.

    Anotacija z okvirjem

  • Annotation reduction

    The tool allows you to remove redundant selection using (other) AI algorithms.

    Odstranitev odvečne anotacije

Project partners

  • University of Toronto, (coordinator: Sanja Fidler, PhD, associate professor at the University of Toronto, director of AI at NVIDIA),
  • Donna Shukaris, (VPRI, innovations & entrepreneurship manager),
  • Abelium d.o.o., (head of development, Marko Boben, PhD)

Media posts and useful links

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