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The Kinetics Human Action Video Dataset

About

We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers.

Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, Andrew Zisserman• 2017

Related benchmarks

TaskDatasetResultRank
Action RecognitionUCF101 (mean of 3 splits)
Accuracy45.4
357
Action RecognitionHMDB-51 (average of three splits)
Top-1 Acc15.9
204
Action RecognitionKinetics
Top-1 Acc57
83
Action RecognitionUCF101 (val)--
42
Action RecognitionKinetics (test)
Top-1 Acc57
25
Verb recognitionEpic-Kitchens (EK)
Top-1 Acc4.25
22
Noun recognitionEgo4D
Top-1 Acc5.89
17
Noun recognitionEpic-Kitchens (EK)
Top-1 Acc8.74
17
Verb recognitionEgo4D
Top-1 Acc2.18
17
Action RecognitionHMDB51 (val)--
17
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