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Taras Lehinevych "Shadows Generation in the Wild"

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Fwdays
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The presence of strong illumination variations in images, shadows, in particular, have been shown to be problematic for a variety of computer vision algorithms such as object recognition and recognition. We'll briefly talk about shadow detection, removal and focus on shadow generation in the wild for data augmentation. Website: https://fwdays.com/en/event/data-science-fwdays-2019/review/shadows-generation-in-the-wild

Technologie
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Shadows Generation in the Wild Taras Lehinevych Rails Reactor
Who am I ML Engineer @ Rails Reactor ML Engineer @ nice try bro :) (censored) Disclaimer! All the provided information based on open publication and datasets
Agenda - Motivation - Generative Adversarial Networks - Cycle GAN - MaskShadowGAN - U-GAT-IT - Summary
What’s wrong with shadows?
What do we want?
Datasets SBU Dataset (link) - this new dataset contains 4,727 images (4,089 train images and 638 test images) with pixel based ground truth. ISTD Dataset (link) - it contains 1870 triplets of shadow, shadow mask and shadow-free image under 135 different scenarios.
Generative Adversarial Networks https://medium.com/datadriveninvestor/generative-adversarial-network-gan-using-keras-ce1c05cfdfd3
GAN objective Generative Adversarial Networks
Conditional GAN Image-to-Image Translation with Conditional Adversarial Networks
Conditional GAN Image-to-Image Translation with Conditional Adversarial Networks z|yx|y
Shadow Detection Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network (2018)
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network (2018)
Shadow detected What next?
Approach #2 - Shadow Detection & Removing Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal (2017)
Shadow Detection & Removing
Approach #3 - Shadow Generation/Augmentation
Cycle GAN - Datasets https://hardikbansal.github.io/CycleGANBlog/
Cycle GAN Unpaired Image-to-Image Translation using Cycle- Consistent Adversarial Networks (2017) https://hardikbansal.github.io/CycleGANBlog/
Cycle GAN
Cycle GAN
Identity Loss
Cycle GAN
Mask-Shadow GAN Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
Mask-ShadowGAN
Mask-ShadowGAN
What to improve? Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
U-GAT-IT U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
U-GAT- IT
U-GAT-IT
Summary
Thanks for your time and attention! Questions?

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