  Typical human actions last several seconds and exhibit characteristic
spatio-temporal structure. Recent methods attempt to capture this structure and
learn action representations with convolutional neural networks. Such
representations, however, are typically learned at the level of a few video
frames failing to model actions at their full temporal extent. In this work we
learn video representations using neural networks with long-term temporal
convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal
extents improve the accuracy of action recognition. We also study the impact of
different low-level representations, such as raw values of video pixels and
optical flow vector fields and demonstrate the importance of high-quality
optical flow estimation for learning accurate action models. We report
state-of-the-art results on two challenging benchmarks for human action
recognition UCF101 (92.7%) and HMDB51 (67.2%).
