Supercharge your Computer Vision models with the TensorFlow Object Detection API

Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer(Cross-posted on the Google Open Source Blog)At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems. Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Since then, this system has generated results for a number of research publications1,2,3,4,5,6,7 and has been put to work in Google products such as NestCam, the similar items and style ideas feature in Image Search and…
Original Post: Supercharge your Computer Vision models with the TensorFlow Object Detection API

The Machine Intelligence Behind Gboard

Posted by Fran├žoise Beaufays, Principal Scientist, Speech and Keyboard Team and Michael Riley, Principal Scientist, Speech and Languages Algorithms TeamMost people spend a significant amount of time each day using mobile-device keyboards: composing emails, texting, engaging in social media, and more. Yet, mobile keyboards are still cumbersome to handle. The average user is roughly 35% slower typing on a mobile device than on a physical keyboard. To change that, we recently provided many exciting improvements to Gboard for Android, working towards our vision of creating an intelligent mechanism that enables faster input while offering suggestions and correcting mistakes, in any language you choose.With the realization that the way a mobile keyboard translates touch inputs into text is similar to how a speech recognition system translates voice inputs into text, we leveraged our experience in Speech Recognition to pursue our vision.…
Original Post: The Machine Intelligence Behind Gboard

Coarse Discourse: A Dataset for Understanding Online Discussions

Posted by Praveen Paritosh, Senior Research Scientist, Ka Wong, Senior Data ScientistEvery day, participants of online communities form and share their opinions, experiences, advice and social support, most of which is expressed freely and without much constraint. These online discussions are often a key resource of information for many important topics, such as parenting, fitness, travel and more. However, these discussions also are intermixed with a clutter of disagreements, humor, flame wars and trolling, requiring readers to filter the content before getting the information they are looking for. And while the field of Information Retrieval actively explores ways to allow users to more efficiently find, navigate and consume this content, there is a lack of shared datasets on forum discussions to aid in understanding these discussions a bit better.To aid researchers in this space, we are releasing the Coarse Discourse…
Original Post: Coarse Discourse: A Dataset for Understanding Online Discussions