Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has
made remarkable progress with development of deep convolutional networks over past …

We propose a novel video frame interpolation algorithm based on asymmetric bilateral
motion estimation (ABME), which synthesizes an intermediate frame between two input …

Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …

Dense 3D reconstruction and ego-motion estimation are key challenges in autonomous
driving and robotics. Compared to the complex, multi-modal systems deployed today, multi …

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 …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

We propose the first stochastic framework to employ uncertainty for RGB-D saliency
detection by learning from the data labeling process. Existing RGB-D saliency detection …

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 propose a new video camouflaged object detection (VCOD) framework that can exploit
both short-term dynamics and long-term temporal consistency to detect camouflaged objects …

Recent research on learned visual descriptors has shown promising improvements in
correspondence estimation, a key component of many 3D vision tasks. However, existing …