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DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks
[article]
2018
arXiv
pre-print
Preserved Fulltext
We propose an action recognition framework using Gen- erative Adversarial Networks. ...
Our model involves train- ing a deep convolutional generative adversarial network (DCGAN) using a large video activity dataset without la- bel information. ...
Generative Adversarial Networks [4] have been used for semi-supervised feature learning particularly after the introduction of Deep Convolutional GANs (or DCGANs) [38] . ...
arXiv:1801.07230v1
fatcat:jadecr4slffrtezim3lrr2klva
t-EVA: Time-Efficient t-SNE Video Annotation
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Placing the same actions from different videos near each other in the two-dimensional space based on feature similarity helps the annotator to group-label video clips. ...
In this work, we propose a time-efficient video annotation method using spatio-temporal feature similarity and t-SNE dimensionality reduction to speed up the annotation process massively. ...
.: Discrimnet: Semi-supervised action recognition
from videos using generative adversarial networks. CoRR abs/1801.07230 (2018),
http://arxiv.org/abs/1801.07230
2. ...
arXiv:2011.13202v1
fatcat:4zsyhvyayveepld4ucoypqm2u4