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June 19, 2022 – June 24, 2022

Microsoft at CVPR 2022

Central Daylight Time (UTC -5)

位置: Hybrid | New Orleans, LA

Microsoft Research ブログ

Research Focus Week of February 19, 2024
Microsoft Research ブログ

In this issue: CaaSPER: vertical autoscaling algorithm dynamically maintains optimal CPU utilization; Improved scene landmark detection for camera localization runs faster, uses less storage; ESUS simplifies usability questionnaires for technical products and services.

On the left, a diagram with three layers, each of which contains a half-transparent image processed from the same image. The processed images are partitioned into several grids, and each grid contains 4 x 4 image patches. From bottom to top, the number of grids in each layer are 4 x 4, 2 x 2, and 1 x 1, respectively. The layers are labeled “4x,” “8x,” and “16x,” respectively, from bottom to top. An arrow joining the three layers points upward to the words “Segmentation” and “Detection” and an ellipsis. Another arrow points from the top layer to the word “classification.” On the right, a bar chart with a blue bar labeled “Swin V1” and an orange bar labeled “Swin V2.” The orange bar is much taller and labeled “3 billion (1,536 x 1,536 resolution)”; the blue bar is labeled “197 million.” An arrow labeled “15x” points upward from the blue bar, indicating the orange bar is 15 times higher than the blue one.
Microsoft Research ブログ

| Han Hu と Baining Guo

Early last year, our research team from the Visual Computing Group (opens in new tab) introduced Swin Transformer (opens in new tab), a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important…