WACV
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Converting text to images for product discovery
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Generative adversarial networks (GANs), which were first introduced in 2014, have proven remarkably successful at generating synthetic images. A GAN ...

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WACV computer vision conference prioritizes real solutions to real problems
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Yesterday at the IEEE Winter Conference on Applications of Computer Vision (WACV), Gérard Medioni, vice president and distinguished scientist at Amazon, ...

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Amazon at WACV: Computer vision is more than labeling pixels
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Gérard Medioni, an Amazon vice president and distinguished scientist, is the general chair at this year’s IEEE Winter Conference on Applications of Computer ...

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Prime Video’s work on sports field registration, recap/intro detection
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Like all of Amazon’s major technology groups, Amazon Prime Video has a dedicated team of scientists who are working constantly to find new ways to delight ...

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WACV: Transformers for video and contrastive learning
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Joe Tighe, senior manager for computer vision at Amazon Web Services, is a coauthor on two papers being presented at this year’s Winter Conference on ...

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Using computer vision to weed out product catalogue errors
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A product page in the Amazon Store will often include links to product variants, which differ by color, size, style, and so on. Sometimes, however, errors ...

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Hierarchical representations improve image retrieval
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Image matching has many practical applications. For instance, image retrieval systems like Amazon’s StyleSnap or the Amazon Shopping app’s Camera Search let ...

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How Prime Video uses machine learning to ensure video quality
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Streaming video can suffer from defects introduced during recording, encoding, packaging, or transmission, so most subscription video services — such as ...

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Improving automatic discrimination of logos with similar texts
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Logo recognition is the task of identifying a specific logo and its location in images or videos. It helps create a safe and trustworthy shopping ...

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More-efficient annotation for semantic segmentation in video
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Most state-of-the-art computer vision models depend on supervised learning, in which labeled data is used for training. But labeling is costly, and the cost ...

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