Data augmentation is one of the leading methods to tackle the problem of few-shot learning, but current synthesis approaches only address the scenario of a single label per image, when in reality real life images may contain multiple objects. The IBM team came up with a novel technique for synthesizing samples with multiple labels.
Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists.
Deep neural networks have demonstrated good results for few-shot learning. However, very few works have investigated the problem of few-shot object detection. A team of IBM researchers developed a novel approach for Distance Metric Learning (DML).
Medical imaging creates tremendous amounts of data: many emergency room radiologists must examine as many as 200 cases each day, and some medical studies contain up to 3,000 images. Each patient’s image collection can contain 250GB of data, ultimately creating collections across organizations that are petabytes in size. Within IBM Research, we see potential in […]
Our team at IBM Research recently developed a new approach for automated video scene detection. Videos are used today for everything from entertainment and marketing to knowledge-sharing, news, and social journaling. Automated scene detection can help consumers and enterprises utilize this video content in new ways. Video scene detection is the task of temporally dividing […]
Cyber-physical systems, or CPS for short, are sophisticated computer devices that work together to perform functions, control physical elements, and respond to human control. They are already being used in auto-pilot systems for aircraft, advanced robotic systems, smart grids, medical monitoring, and search and rescue. In fact, most Internet of Things (IoT) devices are CPS. […]
We can all thank an elementary school teacher for keeping our electronic data safe and secure. Growing up in Argentina, and studying in Israel, not far from where his career started at IBM’s Haifa Lab, IBM Fellow and cryptographer, Hugo M. Krawczyk cites his third-grade teacher as one of his first influences. “She piqued my […]
The other day my son asked me to explain what I do at work. I told him that our team builds technology that can answer your questions just by pointing your phone at what you’re trying to fix or understand. The augmented reality we’re developing will know what it is, and what to do – […]
Vita Bortnikov has had to cross quite a few bridges – from her childhood in Ukraine, to choosing a career path after only two years in Israel, and through her years at IBM. What seemed like a cut-and-dried choice to 18-year-old Vita – mathematics or computers – has led to a successful and satisfying professional […]
Or using cognitive computing to divide a video into scenes Searching through a video for a specific person, scene, or moment usually means a frustrating and painstaking ‘hunt and peck’ process. Wouldn’t it be great if you could just skip to where your 90-year-old grandmother boogies to Justin Timberlake at your wedding, without having to […]
A Q&A with Ezra Silvera Ezra Silvera is an IBM researcher who has been tackling the challenge of massive scale data in the cloud’s virtual machines for over a decade. In plain English, he tries to get groups of computers to work together as one virtual machine, and process petabytes of data, without anyone knowing […]
Tweaking wastewater treatment operations allowed us to dramatically lower costs and reduce waste A city with 2 million people will likely have a wastewater treatment plant (WWTP) that handles hundreds of millions of gallons of sewage in a day. This is about the amount that would fit into all 2 million of those residents’ bathtubs! […]