  Given the vast amounts of video available online, and recent breakthroughs in
object detection with static images, object detection in video offers a
promising new frontier. However, motion blur and compression artifacts cause
substantial frame-level variability, even in videos that appear smooth to the
eye. Additionally, video datasets tend to have sparsely annotated frames. We
present a new framework for improving object detection in videos that captures
temporal context and encourages consistency of predictions. First, we train a
pseudo-labeler, that is, a domain-adapted convolutional neural network for
object detection. The pseudo-labeler is first trained individually on the
subset of labeled frames, and then subsequently applied to all frames. Then we
train a recurrent neural network that takes as input sequences of
pseudo-labeled frames and optimizes an objective that encourages both accuracy
on the target frame and consistency across consecutive frames. The approach
incorporates strong supervision of target frames, weak-supervision on context
frames, and regularization via a smoothness penalty. Our approach achieves mean
Average Precision (mAP) of 68.73, an improvement of 7.1 over the strongest
image-based baselines for the Youtube-Video Objects dataset. Our experiments
demonstrate that neighboring frames can provide valuable information, even
absent labels.
