  Even with the recent advances in convolutional neural networks (CNN) in
various visual recognition tasks, the state-of-the-art action recognition
system still relies on hand crafted motion feature such as optical flow to
achieve the best performance. We propose a multitask learning model
ActionFlowNet to train a single stream network directly from raw pixels to
jointly estimate optical flow while recognizing actions with convolutional
neural networks, capturing both appearance and motion in a single model. We
additionally provide insights to how the quality of the learned optical flow
affects the action recognition. Our model significantly improves action
recognition accuracy by a large margin 31% compared to state-of-the-art
CNN-based action recognition models trained without external large scale data
and additional optical flow input. Without pretraining on large external
labeled datasets, our model, by well exploiting the motion information,
achieves competitive recognition accuracy to the models trained with large
labeled datasets such as ImageNet and Sport-1M.
