In this paper, we present an approach for learning a visual representation from the raw
spatiotemporal signals in videos. Our representation is learned without supervision from …

We introduce a novel self-supervised learning approach to learn representations of videos
that are responsive to changes in the motion dynamics. Our representations can be learned …

We present an unsupervised representation learning approach using videos without
semantic labels. We leverage the temporal coherence as a supervisory signal by formulating …

Visual tempo, which describes how fast an action goes, has shown its potential in
supervised action recognition. In this work, we demonstrate that visual tempo can also serve …

We introduce a new model for representation learning and classification of video
sequences. Our model is based on a convolutional neural network coupled with a novel …

We propose a self-supervised spatiotemporal learning technique which leverages the
chronological order of videos. Our method can learn the spatiotemporal representation of …

Abstract We use Long Short Term Memory (LSTM) networks to learn representations of
video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed …

Understanding temporal information and how the visual world changes over time, is a
fundamental ability of intelligent systems. In video understanding, temporal information is at …

In this paper, we propose a self-supervised method for video representation learning based
on Contrastive Predictive Coding (CPC)[27]. Previously, CPC has been used to learn …

The CNN-encoding of features from entire videos for the representation of human actions
has rarely been addressed. Instead, CNN work has focused on approaches to fuse spatial …