{"id":774328,"date":"2021-09-14T10:10:24","date_gmt":"2021-09-14T17:10:24","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=774328"},"modified":"2023-03-07T15:45:48","modified_gmt":"2023-03-07T23:45:48","slug":"orbit-dataset","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/orbit-dataset\/","title":{"rendered":"ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition"},"content":{"rendered":"

Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in a real-world application of teachable object recognizers for people who are blind\/low vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind\/low-vision on their mobile phones, and the benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the first state-of-the-art on the benchmark and show that there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind\/low-vision community. The dataset is available at https:\/\/doi.org\/10.25383\/city.14294597 (opens in new tab)<\/span><\/a> and the code to run the benchmark at https:\/\/github.com\/microsoft\/ORBIT-Dataset (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack 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Experiences (Tai X)","post_name":"taix","post_type":"msr-project","post_date":"2022-03-31 06:56:26","post_modified":"2024-07-10 09:49:02","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/taix\/","post_excerpt":"The Teachable AI Experiences team (Tai X) aims to innovate teachable AI systems that allow people near or far from the norm to create meaningful personalized experiences for themselves. What we ALL have in common is that we are unique. Millions of people find that they do not fit into one of the coarse-grained buckets that have become the technical underpinning of our AI technologies of today (See Research Talk: Bucket of Me). 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