Samsung Researchers launches CVPR 2020

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Researchers from Samsung Research America also presented interesting findings at the conference. An Efficient Architecture for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones’ focused on key enablers to narrow the gap between DSLR and smartphone camera in terms of bokeh, the narrow depth of field (DoF).

Joining from the Toronto AI Center, researcher Michael Brown and his team introduced the paper titled ‘Deep White-Balance Editing’, which was also selected for an oral presentation. This AI technology corrects white-balance mistakes made in a captured photograph much more accurately than existing photo editing programs. This technology also allows users to accurately adjust the photo’s white-balance color temperature.

What is CVPR?

The Conference on Computer Vision and Pattern Recognition is an annual conference on computer vision and pattern recognition, which is regarded as one of the most important conferences in its field. CVPR considers a wide range of topics related to computer vision and pattern recognition—basically any topic that is extracting structures or answers from images or videos or applying mathematical methods to data to extract or recognize patterns. Common topics include object recognition, image segmentation, motion estimation, 3D reconstruction, and deep learning

Samsung Electronics’ Global Research & Development (R&D) Centers have presented their studies to the CVPR (Computer Vision and Pattern Recognition) introducing new computer vision, deep learning, and AI-related technical researches. Additionally, the studies proposed by researchers from the Samsung Research’s Visual Technology team and Samsung R&D Institute India-Bangalore were also selected by CVPR.

CVPR is the world’s biggest conference on computer engineering and AI. At this year’s conference, held online from June 14 to 19, Samsung Research, an advanced R&D hub within Samsung Electronics’ SET Business and its advanced R&D centers gave presentations on a total of 11 thesis papers. Researchers from the Samsung Moscow AI center and Samsung Toronto AI center were invited to oral presentations, an opportunity given to only 5% of the entire attendees.

At the oral presentation, Pavel Solovev of Samsung Moscow AI Center introduced ‘High-Resolution Daytime Translation without Domain Labels’, which is a technology that changes a high-resolution landscape photograph into scenes from various times of the day using data without domain label. Konstantin Sofiiuk also introduced ‘f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation’, which is a technology that allows a user to simply click an object in a photograph to precisely select and separate it.