  In this paper, we present an approach for learning a visual representation
from the raw spatiotemporal signals in videos. Our representation is learned
without supervision from semantic labels. We formulate our method as an
unsupervised sequential verification task, i.e., we determine whether a
sequence of frames from a video is in the correct temporal order. With this
simple task and no semantic labels, we learn a powerful visual representation
using a Convolutional Neural Network (CNN). The representation contains
complementary information to that learned from supervised image datasets like
ImageNet. Qualitative results show that our method captures information that is
temporally varying, such as human pose. When used as pre-training for action
recognition, our method gives significant gains over learning without external
data on benchmark datasets like UCF101 and HMDB51. To demonstrate its
sensitivity to human pose, we show results for pose estimation on the FLIC and
MPII datasets that are competitive, or better than approaches using
significantly more supervision. Our method can be combined with supervised
representations to provide an additional boost in accuracy.
