  The performance of video action recognition has been significantly boosted by
using motion representations within a two-stream Convolutional Neural Network
(CNN) architecture. However, there are a few challenging problems in action
recognition in real scenarios, e.g., the variations in viewpoints and poses,
and the changes in backgrounds. The domain discrepancy between the training
data and the test data causes the performance drop. To improve the model
robustness, we propose a novel method to determine the task-irrelevant content
in inputs which increases the domain discrepancy. The method is based on a
human parsing model (HP model) which jointly conducts dense correspondence
labelling and semantic part segmentation. The predictions from the HP model
also function as re-rendering the human regions in each video using the same
set of textures to make humans appearances in all classes be the same. A
revised dataset is generated for training and testing and makes the action
recognition model exhibit invariance to the irrelevant content in the inputs.
Moreover, the predictions from the HP model are used to enrich the inputs to
the AR model during both training and testing. Experimental results show that
our proposed model is superior to existing models for action recognition on the
HMDB-51 dataset and the Penn Action dataset.
