Few-shot video object detection

Qi Fan, Chi-Keung Tang, Yu-Wing Tai

Abstract:

We introduce Few-Shot Video Object Detection (FSVOD)
with three contributions to real-world visual learning challenge in our
highly diverse and dynamic world: 1) a large-scale video dataset FSVOD500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to
generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic;
3) a strategically improved Temporal Matching Network (TMN+) for
matching representative query tube features with better discriminative
ability thus achieving higher diversity. Our TPN and TMN+ are jointly
and end-to-end trained. Extensive experiments demonstrate that our
method produces significantly better detection results on two few-shot
video object detection datasets compared to image-based methods and
other naive video-based extensions.