Computer Vision
Enabling computers and devices to understand what they see.
Video
From SqueezeNet to SqueezeBERT: Developing Efficient Deep Neural Networks
Deep neural networks have been trained to interpret images and text at increasingly high levels of accuracy. In many cases, these accuracy improvements are the result of developing increasingly large and computationally-intensive neural network models.…
Video
Hope Speech and Help Speech: Surfacing Positivity Amidst Hate
Tackling online attacks targeting certain individuals, groups of people, or communities is a major modern-day web challenge. Research efforts in hate speech detection thus far have largely focused on identifying and subsequently filtering out negative…
Publication
Representation Learning with Video Deep InfoMax
Tool
Transfer Learning using Adversarially Robust ImageNet models
This repository contains the code and models necessary to replicate the results of our paper: Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman*, Andrew Ilyas*, Logan Engstrom, Ashish Kapoor, Aleksander Madry
Group
Azure Cognitive Services Research
The mission of the Cognitive Services Research group (CSR) is to make fundamental contributions to advancing the state of the art of the most challenging problems in speech, language, and vision both within Microsoft and…
Video
Harnessing high-fidelity simulation for autonomous systems through AirSim
Robots and autonomous systems are playing a significant role in modern times, in both academic research and industrial applications. Handling the constant variability and uncertainty present in the real world is a major challenge for…
Webinar
Harnessing high-fidelity simulation for autonomous systems through AirSim webinar
In this webinar, Sai Vemprala, a Microsoft researcher, will introduce Microsoft AirSim, an open-source, high-fidelity robotics simulator, and he demonstrates how it can help to train robust and generalizable algorithms for autonomy. He will explain the…
