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VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

https://doi.org/10.1109/cvpr52688.2022.00209

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-47969-4_50openalex-snapshot

Increasing Space-Time Resolution in Video

inferredcites →https://doi.org/10.1007/978-3-030-58571-6_36openalex-snapshot

NASA Neural Articulated Shape Approximation

inferredcites →https://doi.org/10.1007/978-3-030-58610-2_38openalex-snapshot

Video Super-Resolution with Recurrent Structure-Detail Network

inferredcites →https://doi.org/10.1007/s11263-018-01144-2openalex-snapshot

Video Enhancement with Task-Oriented Flow

inferredcites →https://doi.org/10.1109/cvpr.2011.5995360openalex-snapshot

Space-time super-resolution from a single video

inferredcites →https://doi.org/10.1109/cvpr.2011.5995614openalex-snapshot

A Bayesian approach to adaptive video super resolution

inferredcites →https://doi.org/10.1109/cvpr.2015.7298747openalex-snapshot

Phase-based frame interpolation for video

inferredcites →https://doi.org/10.1109/cvpr.2017.244openalex-snapshot

Video Frame Interpolation via Adaptive Convolution

inferredcites →https://doi.org/10.1109/cvpr.2017.304openalex-snapshot

Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation

inferredcites →https://doi.org/10.1109/cvpr.2017.33openalex-snapshot

Deep Video Deblurring for Hand-Held Cameras

inferredcites →https://doi.org/10.1109/cvpr.2017.35openalex-snapshot

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

inferredcites →https://doi.org/10.1109/cvpr.2017.502openalex-snapshot

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

inferredcites →https://doi.org/10.1109/cvpr.2018.00183openalex-snapshot

Context-Aware Synthesis for Video Frame Interpolation

inferredcites →https://doi.org/10.1109/cvpr.2018.00340openalex-snapshot

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

inferredcites →https://doi.org/10.1109/cvpr.2018.00931openalex-snapshot

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

inferredcites →https://doi.org/10.1109/cvpr.2018.00938openalex-snapshot

Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

inferredcites →https://doi.org/10.1109/cvpr.2019.00025openalex-snapshot

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

inferredcites →https://doi.org/10.1109/cvpr.2019.00382openalex-snapshot

Depth-Aware Video Frame Interpolation

inferredcites →https://doi.org/10.1109/cvpr.2019.00459openalex-snapshot

Occupancy Networks: Learning 3D Reconstruction in Function Space

inferredcites →https://doi.org/10.1109/cvpr.2019.00609openalex-snapshot

Learning Implicit Fields for Generative Shape Modeling

inferredcites →https://doi.org/10.1109/cvpr.2019.00953openalex-snapshot

Deformable ConvNets V2: More Deformable, Better Results

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00293openalex-snapshot

Space-Time-Aware Multi-Resolution Video Enhancement

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00342openalex-snapshot

TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00343openalex-snapshot

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00491openalex-snapshot

Local Deep Implicit Functions for 3D Shape

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00548openalex-snapshot

Softmax Splatting for Video Frame Interpolation

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00491openalex-snapshot

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00574openalex-snapshot

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00632openalex-snapshot

Temporal Modulation Network for Controllable Space-Time Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00852openalex-snapshot

Learning Continuous Image Representation with Local Implicit Image Function

inferredcites →https://doi.org/10.1109/cvpr46437.2021.01061openalex-snapshot

Adversarial Generation of Continuous Images

inferredcites →https://doi.org/10.1109/cvpr46437.2021.01405openalex-snapshot

Image Generators with Conditionally-Independent Pixel Synthesis

inferredcites →https://doi.org/10.1109/cvprw.2019.00247openalex-snapshot

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2017.37openalex-snapshot

Video Frame Interpolation via Adaptive Separable Convolution

inferredcites →https://doi.org/10.1109/iccv.2017.479openalex-snapshot

Detail-Revealing Deep Video Super-Resolution

inferredcites →https://doi.org/10.1109/iccv.2017.52openalex-snapshot

Flow-Guided Feature Aggregation for Video Object Detection

inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-snapshot

Deformable Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2019.00098openalex-snapshot

Unsupervised Video Interpolation Using Cycle Consistency

inferredcites →https://doi.org/10.1109/iccv.2019.00484openalex-snapshot

Implicit Surface Representations As Layers in Neural Networks

inferredcites →https://doi.org/10.1109/iccv.2019.00630openalex-snapshot

SlowFast Networks for Video Recognition

inferredcites →https://doi.org/10.1109/iccv.2019.00725openalex-snapshot

Learning Shape Templates With Structured Implicit Functions

inferredcites →https://doi.org/10.1109/iccv48922.2021.01404openalex-snapshot

Unconstrained Scene Generation with Locally Conditioned Radiance Fields

inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-snapshot

Image quality assessment: from error visibility to structural similarity

inferredcites →https://doi.org/10.1109/tpami.2010.167openalex-snapshot

Space-Time Super-Resolution Using Graph-Cut Optimization

inferredcites →https://doi.org/10.1609/aaai.v34i07.6788openalex-snapshot

FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.2007.02442openalex-snapshot

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

inferredcites →https://doi.org/10.48550/arxiv.2106.12423openalex-snapshot

Alias-Free Generative Adversarial Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Fast Encoding and Decoding for Implicit Video Representation

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Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors

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Implicit Neural Models to Extract Heart Rate from Video

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DSCVSR: A Lightweight Video Super-Resolution for Arbitrary Magnification

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SAVSR++: An All-Stage Scale-Aware and Temporal Omniscient Framework for Arbitrary-Scale Video Super-Resolution

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FFEINR: flow feature-enhanced implicit neural representation for spatiotemporal super-resolution

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Deformable Attention Network for Efficient Space‐Time Video Super‐Resolution

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3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

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inferred← citeshttps://doi.org/10.3390/e25081167openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/rs15245646openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/rs16234473openalex-snapshot

Self-Activated Implicit Neural Representation for Synthetic Aperture Radar Images

inferred← citeshttps://doi.org/10.3390/rs18193343openalex-snapshot

A Guided Implicit Generative Adversarial CNN–Transformer Framework for Class-Imbalanced Hyperspectral Image Classification

inferred← citeshttps://doi.org/10.3390/s24154801openalex-snapshot

ABDGAN: Arbitrary Time Blur Decomposition Using Critic-Guided TripleGAN

inferred← citeshttps://doi.org/10.5909/jbe.2024.29.7.1168openalex-snapshot

Levels-of-Experts based on Hard Segmentations for Improving Implicit Neural Networks

inferred← citeshttps://doi.org/10.70675/bb1e6237zdce0z4b99z9164z6f369988e1dbopenalex-snapshot

Learning-based algorithms for real-time visual localization of vehicles

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