  Deep neural networks are efficient learning machines which leverage upon a
large amount of manually labeled data for learning discriminative features.
However, acquiring substantial amount of supervised data, especially for videos
can be a tedious job across various computer vision tasks. This necessitates
learning of visual features from videos in an unsupervised setting. In this
paper, we propose a computationally simple, yet effective, framework to learn
spatio-temporal feature embedding from unlabeled videos. We train a
Convolutional 3D Siamese network using positive and negative pairs mined from
videos under certain probabilistic assumptions. Experimental results on three
datasets demonstrate that our proposed framework is able to learn weights which
can be used for same as well as cross dataset and tasks.
