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Found 10 papers, 5 papers with code
Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation
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.
Dynamic Frame Interpolation in Wavelet Domain
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience.
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation
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.
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
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)
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation
Video frame interpolation is a classic and challenging low-level computer vision task.
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
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
FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
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
Unsupervised Motion Representation Enhanced Network for Action Recognition
Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding.
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
Optical flow estimation is an essential step for many real-world computer vision tasks.
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
Significant progress has been made for estimating optical flow using deep neural networks.
Ranked #7 on
Optical Flow Estimation
on KITTI 2012
