Customer-obsessed science
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks.
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July 30, 20268 min read
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July 9, 202610 min read
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Featured news
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ICCV 2019 Workshop on Computer Vision for Fashion, Art and Design2019We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semantically and visually similar images are closer and dissimilar images are further from one another. We present a weakly supervised adaptive triplet loss (ATL) capable of capturing fine-grained semantic similarity that encourages
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KDD 20192019The Amazon video homepage is the primary gateway for customers looking to explore the large collection of content, and finding something interesting to watch. Typically, the page is personalized for a customer, and consists of a series of widgets or carousels, with each widget containing multiple items (e.g., movies, TV shows etc). Ranking the widgets needs to maximize relevance, and maintain diversity,
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ICDAR 20192019Some robot systems need to interactively auto-type on touch screens using a digital camera as the input source. To do so, it is important to have an algorithm that reliably detects the keyboard region and locates and recognizes its characters from an image. However, today most research efforts in the optical character recognition (OCR) area is focused on scene text detection and recognition. Though these
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3DV 20192019We propose a novel computer vision system for reconstructing 3D body shapes from 2D images with the goal of producing highly accurate anthropomorphic measurements from a pair of images. We adopt a supervised learning approach that maps silhouette images to 3D body shapes via a convolutional neural network (CNN). We propose three key improvements over previous approaches: (1) Large-scale realistic synthetic
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Foresight Journal of Applied Forecasting2019Since Rob Hyndman & Stephan Kolassa wrote their Foresight article in 2010 on “Free Open-Source Forecasting Using R” much has happened. The forecast package for the R statistical language (Hyndman & Khandakar, 2008), abbreviated to “R Forecast package” in the following, was the main focus of the article then. Now, it is the reference implementation of many classical forecasting methods such as exponential
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