  Despite significant progress in semi-supervised learning for image object
detection, several key issues are yet to be addressed for video object
detection: (1) Achieving good performance for supervised video object detection
greatly depends on the availability of annotated frames. (2) Despite having
large inter-frame correlations in a video, collecting annotations for a large
number of frames per video is expensive, time-consuming, and often redundant.
(3) Existing semi-supervised techniques on static images can hardly exploit the
temporal motion dynamics inherently present in videos. In this paper, we
introduce SSVOD, an end-to-end semi-supervised video object detection framework
that exploits motion dynamics of videos to utilize large-scale unlabeled frames
with sparse annotations. To selectively assemble robust pseudo-labels across
groups of frames, we introduce \textit{flow-warped predictions} from nearby
frames for temporal-consistency estimation. In particular, we introduce
cross-IoU and cross-divergence based selection methods over a set of estimated
predictions to include robust pseudo-labels for bounding boxes and class
labels, respectively. To strike a balance between confirmation bias and
uncertainty noise in pseudo-labels, we propose confidence threshold based
combination of hard and soft pseudo-labels. Our method achieves significant
performance improvements over existing methods on ImageNet-VID, Epic-KITCHENS,
and YouTube-VIS datasets. Code and pre-trained models will be released.
