Category Added in a WPeMatico Campaign
Posted by Shaina Mehta, Program Manager, Google Google is proud to be a Platinum Sponsor of the European Conference on Computer Vision (ECCV 2022), a premier forum for the dissemination of research in computer vision and machine learning (ML). This year, ECCV 2022 will be held as a hybrid event, in person in Tel Aviv, …
An estimated 12 million people have fled their homes since Russia’s brutal invasion of Ukraine began, according to the United Nations — resulting in one of the largest humanitarian catastrophes since the Second World War. With countries in Europe and beyond stepping up to come to the assistance of Ukraine and those displaced, the UK government launched …
Read more “Ensuring the Resettling and Safeguarding of Refugees Fleeing the War in Ukraine”
Posted by Wenhao Yu, Research Scientist, Robotics at Google, and Kuang-Huei Lee, Research Engineer, Google Research, Brain team Evolution strategy (ES) is a family of optimization techniques inspired by the ideas of natural selection: a population of candidate solutions are usually evolved over generations to better adapt to an optimization objective. ES has been applied …
Posted by Junjie Ke, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Understanding the aesthetic and technical quality of images is important for providing a better user visual experience. Image quality assessment (IQA) uses models to build a bridge between an image and a user’s subjective perception of its quality. In …
Read more “MUSIQ: Assessing Image Aesthetic and Technical Quality with Multi-scale Transformers”
Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve successfully collected data, you still have …
Read more “Create synthetic data for computer vision pipelines on AWS”
Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your own containers with custom AI/ML algorithms. …
Read more “Enable CI/CD of multi-Region Amazon SageMaker endpoints”
There’s a lot of talk about the positive impact that machine learning can have on our lives as citizens and consumers. But did you know that it can reduce complexity and toil for cloud administrators? Google Cloud Active Assist uses data, intelligence, and ML to optimize the security, performance, and cost of your cloud environment. …
We’ve all been there— asking a voice assistant to play a song, launch an app, or answer a question, but the assistant doesn’t comply. Maybe it’s a network outage, or maybe you’re in the middle of nowhere, far away from coverage—either way the result is the same: the voice assistant can’t connect to the server …
Read more “Run Google Cloud Speech AI locally, no internet connection required”
(An English-language version of this post can be read here.) Nota del editor: Este es el tercer post de Palantir Explained, una serie que explora una selección de temas, incluido nuestro enfoque hacia la privacidad, la seguridad, y la seguridad de la IA/ML, entre otros. Las entradas anteriores exploran nuestro modelo de negocio y los controles …
Posted by Manoj Kumar, Research Engineer, and Ekin Dogus Cubuk, Research Scientist, Google Research The task of determining the similarity between images is an open problem in computer vision and is crucial for evaluating the realism of machine-generated images. Though there are a number of straightforward methods of estimating image similarity (e.g., low-level metrics that …
Read more “Do Modern ImageNet Classifiers Accurately Predict Perceptual Similarity?”