Online Model Distillation for Efficient Video Inference

Understand

High-quality computer vision models typically address the problem of understanding the general distribution of real-world images.

• However, most cameras observe only a very small fraction of this distribution.
• This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera.
• In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream.