  Video object detection is more challenging compared to image object
detection. Previous works proved that applying object detector frame by frame
is not only slow but also inaccurate. Visual clues get weakened by defocus and
motion blur, causing failure on corresponding frames. Multi-frame feature
fusion methods proved effective in improving the accuracy, but they
dramatically sacrifice the speed. Feature propagation based methods proved
effective in improving the speed, but they sacrifice the accuracy. So is it
possible to improve speed and performance simultaneously?
  Inspired by how human utilize impression to recognize objects from blurry
frames, we propose Impression Network that embodies a natural and efficient
feature aggregation mechanism. In our framework, an impression feature is
established by iteratively absorbing sparsely extracted frame features. The
impression feature is propagated all the way down the video, helping enhance
features of low-quality frames. This impression mechanism makes it possible to
perform long-range multi-frame feature fusion among sparse keyframes with
minimal overhead. It significantly improves per-frame detection baseline on
ImageNet VID while being 3 times faster (20 fps). We hope Impression Network
can provide a new perspective on video feature enhancement. Code will be made
available.
