  This paper accelerates video perception, such as semantic segmentation and
human pose estimation, by levering cross-frame redundancies. Unlike the
existing approaches, which avoid redundant computations by warping the past
features using optical-flow or by performing sparse convolutions on frame
differences, we approach the problem from a new perspective: low-bit
quantization. We observe that residuals, as the difference in network
activations between two neighboring frames, exhibit properties that make them
highly quantizable. Based on this observation, we propose a novel quantization
scheme for video networks coined as Residual Quantization. ResQ extends the
standard, frame-by-frame, quantization scheme by incorporating temporal
dependencies that lead to better performance in terms of accuracy vs.
bit-width. Furthermore, we extend our model to dynamically adjust the bit-width
proportional to the amount of changes in the video. We demonstrate the
superiority of our model, against the standard quantization and existing
efficient video perception models, using various architectures on semantic
segmentation and human pose estimation benchmarks.
