36 citations · 57 across the 7 of their papers we have counts for
7 papers
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation
Lingtong Kong, Jinfeng Liu, Jie Yang
Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for…
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
Lingtong Kong, Jie Yang
Recent works have shown that optical flow can be learned by deep networks from unlabelled image pairs based on brightness constancy assumption and smoothness prior. Current approac…
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
Lingtong Kong, Boyuan Jiang, Donghao Luo +5
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters…
FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
Lingtong Kong, Chunhua Shen, Jie Yang
Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical fl…
Unsupervised Motion Representation Enhanced Network for Action Recognition
Xiaohang Yang, Lingtong Kong, Jie Yang
Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding. However, the TV-L1 method, an e…
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
Lingtong Kong, Xiaohang Yang, Jie Yang
Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal co…