1 code implementation • 11 Sep 2023 • Jinfeng Liu, Lingtong Kong, Jie Yang, Wei Liu
Additionally, we introduce the detail-enhanced DepthNet with an extra full-scale branch in the encoder and a grid decoder to enhance the restoration of fine details in depth maps.
1 code implementation • 7 Sep 2023 • Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Ying Tai, Chengjie Wang, Jie Yang
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience.
no code implementations • 11 Nov 2022 • 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 target frame generation.
1 code implementation • 11 Nov 2022 • Lingtong Kong, Jie Yang
To break the dilemma, we propose a novel mutual distillation framework to transfer reliable knowledge back and forth between the teacher and student networks for alternate improvement.
Ranked #1 on Optical Flow Estimation on KITTI 2015 unsupervised (Fl-all metric)
no code implementations • 1 Aug 2022 • Jinfeng Liu, Lingtong Kong, Jie Yang
Video frame interpolation is a classic and challenging low-level computer vision task.
2 code implementations • CVPR 2022 • Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, Jie Yang
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications.
Ranked #1 on Video Frame Interpolation on Middlebury
2 code implementations • 8 Mar 2021 • Lingtong Kong, Chunhua Shen, Jie Yang
Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only 1/10 computation of comparable networks to achieve on par accuracy.
Ranked #12 on Optical Flow Estimation on KITTI 2012
no code implementations • 5 Mar 2021 • 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.
no code implementations • 31 Jan 2021 • Lingtong Kong, Xiaohang Yang, Jie Yang
Optical flow estimation is an essential step for many real-world computer vision tasks.
no code implementations • 22 Jun 2020 • Lingtong Kong, Jie Yang
Significant progress has been made for estimating optical flow using deep neural networks.
Ranked #7 on Optical Flow Estimation on KITTI 2012