  We propose a Spatiotemporal Sampling Network (STSN) that uses deformable
convolutions across time for object detection in videos. Our STSN performs
object detection in a video frame by learning to spatially sample features from
the adjacent frames. This naturally renders the approach robust to occlusion or
motion blur in individual frames. Our framework does not require additional
supervision, as it optimizes sampling locations directly with respect to object
detection performance. Our STSN outperforms the state-of-the-art on the
ImageNet VID dataset and compared to prior video object detection methods it
uses a simpler design, and does not require optical flow data for training.
