  In this paper, we propose a spatio-temporal contextual network, STC-Flow, for
optical flow estimation. Unlike previous optical flow estimation approaches
with local pyramid feature extraction and multi-level correlation, we propose a
contextual relation exploration architecture by capturing rich long-range
dependencies in spatial and temporal dimensions. Specifically, STC-Flow
contains three key context modules - pyramidal spatial context module, temporal
context correlation module and recurrent residual contextual upsampling module,
to build the relationship in each stage of feature extraction, correlation, and
flow reconstruction, respectively. Experimental results indicate that the
proposed scheme achieves the state-of-the-art performance of two-frame based
methods on the Sintel dataset and the KITTI 2012/2015 datasets.
