Diagram showing GODEL’s architecture. The environment of the dialog system consists of both structured and unstructured content, which it uses to retrieve information. This source content, which we term “grounding,” is updated and repeatedly used by GODEL to produce a new response after each user input.
Microsoft Research Blog

| Baolin Peng, Michel Galley, Lars Liden, Chris Brockett, Zhou Yu, and Jianfeng Gao

They make restaurant recommendations, help us pay bills, and remind us of appointments. Many people have come to rely on virtual assistants and chatbots to perform a wide range of routine tasks. But what if a single dialog agent, the…

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编者按：国际计算机视觉与模式识别会议（CVPR）是人工智能领域最具学术影响力的顶级会议之一。微软亚洲研究院也在4月成功举办了 CVPR 2022 论文分享会。今天，我们为大家精选了8篇微软亚洲研究院被 CVPR 2022 收录的优秀论文，带你探索计算机视觉领域的热点前沿！欢迎感兴趣的读者阅读论文原文。 论文链接：https://arxiv.org/abs/2203.09208 视频复原任务非常依赖...

In the news | Microsoft On the Issues

Editor’s note: Today Microsoft published a new intelligence report, Defending Ukraine: Early Lessons from the Cyber War. This report represents research conducted by Microsoft’s threat intelligence and data science teams with the goal of sharpening our understanding of the threat landscape in…

Awards | Doctoral Dissertation

Li Zhong was recognized as one of Peking University's Outstanding Doctoral Dissertations in 2022 in Computational Mathematics for the paper, "Machine Learning Theory of Dynamical Systems".

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 Blog

| Han Hu and Baining Guo

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

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In the news | Microsoft Innovation

Learn how Swin Transformer surpasses previously dominant CNN (convolutional neural network) architectures in computer vision. Presented by Han Hu, Principal Researcher and Research Manager from Microsoft Research Asia.

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As a top international academic conference in the field of natural language processing, ACL attracts paper submissions and conference participation from a large number of scholars every year. This year's ACL conference was held from May 22nd to May 27th.…