  Video frame interpolation is an increasingly important research task with
several key industrial applications in the video coding, broadcast and
production sectors. Recently, transformers have been introduced to the field
resulting in substantial performance gains. However, this comes at a cost of
greatly increased memory usage, training and inference time. In this paper, a
novel method integrating a transformer encoder and convolutional features is
proposed. This network reduces the memory burden by close to 50% and runs up to
four times faster during inference time compared to existing transformer-based
interpolation methods. A dual-encoder architecture is introduced which combines
the strength of convolutions in modelling local correlations with those of the
transformer for long-range dependencies. Quantitative evaluations are conducted
on various benchmarks with complex motion to showcase the robustness of the
proposed method, achieving competitive performance compared to state-of-the-art
interpolation networks.
