  Optical flow estimation is a classical yet challenging task in computer
vision. One of the essential factors in accurately predicting optical flow is
to alleviate occlusions between frames. However, it is still a thorny problem
for current top-performing optical flow estimation methods due to insufficient
local evidence to model occluded areas. In this paper, we propose the Super
Kernel Flow Network (SKFlow), a CNN architecture to ameliorate the impacts of
occlusions on optical flow estimation. SKFlow benefits from the super kernels
which bring enlarged receptive fields to complement the absent matching
information and recover the occluded motions. We present efficient super kernel
designs by utilizing conical connections and hybrid depth-wise convolutions.
Extensive experiments demonstrate the effectiveness of SKFlow on multiple
benchmarks, especially in the occluded areas. Without pre-trained backbones on
ImageNet and with a modest increase in computation, SKFlow achieves compelling
performance and ranks $\textbf{1st}$ among currently published methods on the
Sintel benchmark. On the challenging Sintel clean and final passes (test),
SKFlow surpasses the best-published result in the unmatched areas ($7.96$ and
$12.50$) by $9.09\%$ and $7.92\%$. The code is available at
\href{https://github.com/littlespray/SKFlow}{https://github.com/littlespray/SKFlow}.
