We present StreamDEQ, a method that infers frame-wise representations on
videos with minimal per-frame computation. In contrast to conventional methods
where compute time grows at least linearly with the network depth, we aim to
update the representations in a continuous manner. For this purpose, we
leverage the recently emerging implicit layer models, which infer the
representation of an image by solving a fixed-point problem. Our main insight
is to leverage the slowly changing nature of videos and use the previous frame
representation as an initial condition on each frame. This scheme effectively
recycles the recent inference computations and greatly reduces the needed
processing time. Through extensive experimental analysis, we show that
StreamDEQ is able to recover near-optimal representations in a few frames time,
and maintain an up-to-date representation throughout the video duration. Our
experiments on video semantic segmentation and video object detection show that
StreamDEQ achieves on par accuracy with the baseline (standard MDEQ) while
being more than $3\times$ faster. Code and additional results are available at
this https URL

StreamDEQ 是一种使用隐式层模型连续推断每帧视频表示且利用先前帧的表示作为初始条件的方法，通过实验证明，StreamDEQ 在几帧的实验时间内准确地恢复了近乎最优的表示，并且可以在视频显示期间保持最新的表示。