  How to effectively explore the colors of reference exemplars and propagate
them to colorize each frame is vital for exemplar-based video colorization. In
this paper, we present an effective BiSTNet to explore colors of reference
exemplars and utilize them to help video colorization by a bidirectional
temporal feature fusion with the guidance of semantic image prior. We first
establish the semantic correspondence between each frame and the reference
exemplars in deep feature space to explore color information from reference
exemplars. Then, to better propagate the colors of reference exemplars into
each frame and avoid the inaccurate matches colors from exemplars we develop a
simple yet effective bidirectional temporal feature fusion module to better
colorize each frame. We note that there usually exist color-bleeding artifacts
around the boundaries of the important objects in videos. To overcome this
problem, we further develop a mixed expert block to extract semantic
information for modeling the object boundaries of frames so that the semantic
image prior can better guide the colorization process for better performance.
In addition, we develop a multi-scale recurrent block to progressively colorize
frames in a coarse-to-fine manner. Extensive experimental results demonstrate
that the proposed BiSTNet performs favorably against state-of-the-art methods
on the benchmark datasets. Our code will be made available at
\url{https://yyang181.github.io/BiSTNet/}
