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Learning to represent videos is a very challenging task both algorithmically and computationally.
Dynamics of human body skeletons convey significant information for human action recognition.
Ranked #2 on Action Recognition on IRD
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Ranked #3 on Multimodal Activity Recognition on EV-Action
We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Ranked #1 on Multimodal Activity Recognition on EV-Action
We report state-of-the-art or comparable results on video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA.
Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action.
Ranked #2 on Skeleton Based Action Recognition on MSR Action3D
To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.
Ranked #4 on Multimodal Activity Recognition on EV-Action
We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities.
Ranked #3 on Multimodal Activity Recognition on UTD-MHAD