  Exemplar-based video colorization is an essential technique for applications
like old movie restoration. Although recent methods perform well in still
scenes or scenes with regular movement, they always lack robustness in moving
scenes due to their weak ability in modeling long-term dependency both
spatially and temporally, leading to color fading, color discontinuity or other
artifacts. To solve this problem, we propose an exemplar-based video
colorization framework with long-term spatiotemporal dependency. To enhance the
long-term spatial dependency, a parallelized CNN-Transformer block and a double
head non-local operation are designed. The proposed CNN-Transformer block can
better incorporate long-term spatial dependency with local texture and
structural features, and the double head non-local operation further leverages
the performance of augmented feature. While for long-term temporal dependency
enhancement, we further introduce the novel linkage subnet. The linkage subnet
propagate motion information across adjacent frame blocks and help to maintain
temporal continuity. Experiments demonstrate that our model outperforms recent
state-of-the-art methods both quantitatively and qualitatively. Also, our model
can generate more colorful, realistic and stabilized results, especially for
scenes where objects change greatly and irregularly.
