  We propose a semi-supervised learning approach for video classification,
VideoSSL, using convolutional neural networks (CNN). Like other computer vision
tasks, existing supervised video classification methods demand a large amount
of labeled data to attain good performance. However, annotation of a large
dataset is expensive and time consuming. To minimize the dependence on a large
annotated dataset, our proposed semi-supervised method trains from a small
number of labeled examples and exploits two regulatory signals from unlabeled
data. The first signal is the pseudo-labels of unlabeled examples computed from
the confidences of the CNN being trained. The other is the normalized
probabilities, as predicted by an image classifier CNN, that captures the
information about appearances of the interesting objects in the video. We show
that, under the supervision of these guiding signals from unlabeled examples, a
video classification CNN can achieve impressive performances utilizing a small
fraction of annotated examples on three publicly available datasets: UCF101,
HMDB51 and Kinetics.
