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Datasets:
SitongGong
/
EgoExoLearn

Tasks:
Video Classification
Modalities:
Video
Languages:
English
Size:
< 1K
Tags:
biology
chemistry
medical
kitchen
cooking
Libraries:
Datasets
License:
Dataset card Data Studio Files
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1
- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ task_categories:
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+ - video-classification
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+ language:
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+ - en
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+ tags:
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+ - biology
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+ - chemistry
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+ - medical
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+ - kitchen
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+ - cooking
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ NOTE: Videos in huggingface are unprocessed, full-size videos. For benchmark and gaze alignment, we use processed 25fps videos. For processed data and code for benchmark, please visit the [github page](https://github.com/OpenGVLab/EgoExoLearn).
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+
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+ # EgoExoLearn
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+ This repository contains the video data of the following paper:
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+ > **EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World**<br>
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+ > [Yifei Huang](https://hyf015.github.io/), [Guo Chen](https://scholar.google.com/citations?user=lRj3moAAAAAJ), [Jilan Xu](https://scholar.google.com/citations?user=mf2U64IAAAAJ), [Mingfang Zhang](https://scholar.google.com/citations?user=KnQO5GcAAAAJ), [Lijin Yang](), [Baoqi Pei](), [Hongjie Zhang](https://scholar.google.com/citations?user=Zl_2sZYAAAAJ), [Lu Dong](), [Yali Wang](https://scholar.google.com/citations?hl=en&user=hD948dkAAAAJ), [Limin Wang](https://wanglimin.github.io), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/index.html)<br>
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+ > IEEE/CVF Conference on Computer Vision and Pattern Recognition (**CVPR**), 2024<be>
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+
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+ EgoExoLearn is a dataset that emulates the human demonstration following process, in which individuals record egocentric videos as they execute tasks guided by exocentric-view demonstration videos. Focusing on the potential applications in daily assistance and professional support, EgoExoLearn contains egocentric and demonstration video data spanning 120 hours captured in daily life scenarios and specialized laboratories. Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints.
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