36 citations · 80 across the 13 of their papers we have counts for
13 papers
Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videos
Jinfeng Liu, Lingtong Kong, Mi Zhou +2
We introduce Mono4DGS-HDR, the first system for reconstructing renderable 4D high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR) videos captured with alt…
SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging
Lingtong Kong, Bo Li, Yike Xiong +3
Multi-exposure High Dynamic Range (HDR) imaging is a challenging task when facing truncated texture and complex motion. Existing deep learning-based methods have achieved great suc…
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation
Jinfeng Liu, Lingtong Kong, Bo Li +3
Self-supervised monocular depth estimation has gathered notable interest since it can liberate training from dependency on depth annotations. In monocular video training case, rece…
Dynamic Frame Interpolation in Wavelet Domain
Lingtong Kong, Boyuan Jiang, Donghao Luo +4
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by em…
Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation
Jinfeng Liu, Lingtong Kong, Jie Yang +1
Depth estimation plays an important role in the robotic perception system. Self-supervised monocular paradigm has gained significant attention since it can free training from the r…
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…
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation
Jinfeng Liu, Lingtong Kong, Jie Yang
Video frame interpolation is a classic and challenging low-level computer vision task. Recently, deep learning based methods have achieved impressive results, and it has been prove…
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…
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
Lingtong Kong, Jie Yang
Significant progress has been made for estimating optical flow using deep neural networks. Advanced deep models achieve accurate flow estimation often with a considerable computati…