activity
20162022
most citedRevisiting Temporal Modeling for Video Super-resolution

63 citations · 192 across the 17 of their papers we have counts for

collaborators

27 papers

cs.CV20226 cited

AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement

Canqian Yang, Meiguang Jin, Xu Jia +2

The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretiz…

cs.CV20223 cited

Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

Takashi Isobe, Xu Jia, Xin Tao +6

Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…

cs.CV2022

FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty

Liqian Ma, Stamatios Georgoulis, Xu Jia +1

The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and ro…

cs.CV20221 cited

TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation

Weihua He, Kaichao You, Zhendong Qiao +6

Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted s…

cs.CV20221 cited

Learning Enriched Illuminants for Cross and Single Sensor Color Constancy

Xiaodong Cun, Zhendong Wang, Chi-Man Pun +4

Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a c…

cs.CV20221 cited

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation

Ruihuang Li, Shuai Li, Chenhang He +3

Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…