  Self-supervised tasks have been utilized to build useful representations that
can be used in downstream tasks when the annotation is unavailable. In this
paper, we introduce a self-supervised video representation learning method
based on the multi-transformation classification to efficiently classify human
actions. Self-supervised learning on various transformations not only provides
richer contextual information but also enables the visual representation more
robust to the transforms. The spatio-temporal representation of the video is
learned in a self-supervised manner by classifying seven different
transformations i.e. rotation, clip inversion, permutation, split, join
transformation, color switch, frame replacement, noise addition. First, seven
different video transformations are applied to video clips. Then the 3D
convolutional neural networks are utilized to extract features for clips and
these features are processed to classify the pseudo-labels. We use the learned
models in pretext tasks as the pre-trained models and fine-tune them to
recognize human actions in the downstream task. We have conducted the
experiments on UCF101 and HMDB51 datasets together with C3D and 3D Resnet-18 as
backbone networks. The experimental results have shown that our proposed
framework is outperformed other SOTA self-supervised action recognition
approaches. The code will be made publicly available.
