Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Abstract Optical Flow Estimation is an essential component for many image processing
techniques. This field of research in computer vision has seen an amazing development in …

Prevailing video frame interpolation algorithms, that generate the intermediate frames from
consecutive inputs, typically rely on complex model architectures with heavy parameters or …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

We present a self-supervised learning approach for optical flow. Our method distills reliable
flow estimations from non-occluded pixels, and uses these predictions as ground truth to …

Motion analysis is one of the most fundamental and challenging problems in the field of
computer vision, which can be widely applied in many areas, such as autonomous driving …

We systematically compare and analyze a set of key components in unsupervised optical
flow to identify which photometric loss, occlusion handling, and smoothness regularization is …

Unsupervised learning of optical flow, which leverages the supervision from view synthesis,
has emerged as a promising alternative to supervised methods. However, the objective of …

We present SMURF, a method for unsupervised learning of optical flow that improves state
of the art on all benchmarks by 36% to 40% and even outperforms several supervised …

We present an unsupervised learning approach for optical flow estimation by improving the
upsampling and learning of pyramid network. We design a self-guided upsample module to …