Semantic Segmentation is a key component in many practical applications, and we humans have an innate understanding of the world around us where if someone points to something we can immediately say what that object is. Reframing, given an image we can say what each pixel in that image belongs to, this ability is important in making many decisions and thus transferring this understanding to Machines is crucial as we want machines to understand the world around it and take decisions based on its environment. Semantic Segmentation or labelling each pixel in an image is how we make a machine understand its surroundings based on an image.

In this talk, I briefly go over the techniques that were being used in the past and are used now and some techniques that are taking the light away, from State of the Art models like DeepLabv3+

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Outline/Structure of the Tutorial

In this presentation, I will talk about the world of Semantic Segmentation and how algorithms and approaches have evolved over time.

Below are some key points that I wish to cover:

  • What is Semantic Segmentation? [10 mins]
    • An introduction to Semantic
    • Applications
    • A very brief overview of the approaches covered before Deep Learning
  • FCN - Fully Convolutional Neural Network for Semantic Segmentation [15 mins]
    • Walkthrough to implement the Architecture along with the code
    • Results achieved using this architecture
  • UNet [15 mins]
    • Architecture Explained along with code
    • Results achieved using this architecture
  • PSPNet [15 mins]
    • Architecture Explained of PSPNet along with the code
    • Results achieved (PSPNet SOTA results on real time semantic segmentation)
  • DeepLab [15 mins]
    • Explaining how the architecture of DeepLabv1 - v3+ evovled
    • Walkthrough the code to implement the architecture
    • Results achieved using this architecture
  • Looking at the Future - EncNet and FastFCN [20 mins]
    • Architecture Design Changes
    • Promising State of the Art Results
  • Conclusion

Note: Timings mentioned are might be updated.

Learning Outcome

I think people who attend this will go out with a sound knowledge of what's going on in the Semantic Segmentation World and which architecture to go for when building their next Semantic Segmentation Project.

Target Audience

Anybody with the given prerequisites

Prerequisites for Attendees

  • Basic Understanding of Deep Learning and Machine Learning
  • What Aritficial Neural Networks are and how they work
  • What Convolutional Neural Networks are and how they work
schedule Submitted 2 weeks ago

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