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Title: GitHub - microsoft/ORBIT-Dataset: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data. · GitHub

Open Graph Title: GitHub - microsoft/ORBIT-Dataset: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.

X Title: GitHub - microsoft/ORBIT-Dataset: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.

Description: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data. - microsoft/ORBIT-Dataset

Open Graph Description: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object ...

X Description: The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object ...

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https://github.com/microsoft/ORBIT-Dataset#orbit-a-real-world-few-shot-dataset-for-teachable-object-recognition
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognitionhttps://arxiv.org/abs/2104.03841
Memory Efficient Meta-Learning with Large Imageshttps://arxiv.org/abs/2107.01105
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