{"list":"other","total":102,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1611.07004","title":"Image-to-Image Translation with Conditional Adversarial Networks","created":"2016-11-21","cited_by_count":4527},{"id":"2012.09841","title":"Taming Transformers for High-Resolution Image Synthesis","created":"2020-12-17","cited_by_count":3253},{"id":"2003.12039","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","created":"2020-03-26","cited_by_count":2415},{"id":"2206.00927","title":"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps","created":"2022-06-02","cited_by_count":1979},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"2104.07636","title":"Image Super-Resolution via Iterative Refinement","created":"2021-04-15","cited_by_count":1511},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"2201.09865","title":"RePaint: Inpainting using Denoising Diffusion Probabilistic Models","created":"2022-01-24","cited_by_count":1430},{"id":"2106.15282","title":"Cascaded Diffusion Models for High Fidelity Image Generation","created":"2021-05-30","cited_by_count":1163},{"id":"2108.05997","title":"MUSIQ: Multi-scale Image Quality Transformer","created":"2021-08-12","cited_by_count":1081},{"id":"2311.18828","title":"One-step Diffusion with Distribution Matching Distillation","created":"2023-11-30","cited_by_count":1019},{"id":"2107.10833","title":"Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data","created":"2021-07-22","cited_by_count":970},{"id":"2211.01095","title":"DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models","created":"2022-11-02","cited_by_count":938},{"id":"2310.04378","title":"Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference","created":"2023-10-06","cited_by_count":846},{"id":"2004.07728","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","created":"2020-04-16","cited_by_count":811},{"id":"2311.17042","title":"Adversarial Diffusion Distillation","created":"2023-11-28","cited_by_count":759},{"id":"2210.03142","title":"On Distillation of Guided Diffusion Models","created":"2022-10-06","cited_by_count":725},{"id":"2405.14867","title":"Improved Distribution Matching Distillation for Fast Image Synthesis","created":"2024-05-23","cited_by_count":684},{"id":"2407.01392","title":"Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion","created":"2024-07-01","cited_by_count":681},{"id":"1811.11168","title":"Deformable ConvNets v2: More Deformable, Better Results","created":"2018-11-27","cited_by_count":670},{"id":"2506.08009","title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","created":"2025-06-09","cited_by_count":625},{"id":"1711.09078","title":"Video Enhancement with Task-Oriented Flow","created":"2017-11-24","cited_by_count":619},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"2103.14006","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","created":"2021-03-25","cited_by_count":529},{"id":"1905.02716","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","created":"2019-05-07","cited_by_count":494},{"id":"2310.02279","title":"Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion","created":"2023-10-01","cited_by_count":459},{"id":"2310.14189","title":"Improved Techniques for Training Consistency Models","created":"2023-10-22","cited_by_count":439},{"id":"2412.07772","title":"From Slow Bidirectional to Fast Autoregressive Video Diffusion Models","created":"2024-12-10","cited_by_count":433},{"id":"2309.06380","title":"InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation","created":"2023-09-12","cited_by_count":395},{"id":"2410.05954","title":"Pyramidal Flow Matching for Efficient Video Generative Modeling","created":"2024-10-08","cited_by_count":366},{"id":"2104.13371","title":"BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment","created":"2021-04-27","cited_by_count":342},{"id":"2012.02181","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","created":"2020-12-03","cited_by_count":304},{"id":"2410.11081","title":"Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models","created":"2024-10-14","cited_by_count":283},{"id":"2403.12015","title":"Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation","created":"2024-03-18","cited_by_count":279},{"id":"2402.13929","title":"SDXL-Lightning: Progressive Adversarial Diffusion Distillation","created":"2024-02-21","cited_by_count":266},{"id":"1812.02898","title":"TDAN: Temporally Deformable Alignment Network for Video Super-Resolution","created":"2018-12-07","cited_by_count":226},{"id":"2201.12288","title":"VRT: A Video Restoration Transformer","created":"2022-01-28","cited_by_count":220},{"id":"2404.04057","title":"Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation","created":"2024-04-05","cited_by_count":214},{"id":"2206.02146","title":"Recurrent Video Restoration Transformer with Guided Deformable Attention","created":"2022-06-05","cited_by_count":170},{"id":"2311.09257","title":"UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs","created":"2023-11-14","cited_by_count":166},{"id":"2310.13268","title":"DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics","created":"2023-10-20","cited_by_count":161},{"id":"1801.04590","title":"Frame-Recurrent Video Super-Resolution","created":"2018-01-14","cited_by_count":160},{"id":"2404.13686","title":"Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis","created":"2024-04-21","cited_by_count":156},{"id":"1612.05890","title":"Learning a No-Reference Quality Metric for Single-Image Super-Resolution","created":"2016-12-18","cited_by_count":144},{"id":"2406.14548","title":"Consistency Models Made Easy","created":"2024-06-20","cited_by_count":144},{"id":"2405.11473","title":"FIFO-Diffusion: Generating Infinite Videos from Text without Training","created":"2024-05-19","cited_by_count":137},{"id":"2111.12704","title":"Investigating Tradeoffs in Real-World Video Super-Resolution","created":"2021-11-24","cited_by_count":134},{"id":"2008.00455","title":"Video Super-Resolution with Recurrent Structure-Detail Network","created":"2020-08-02","cited_by_count":116},{"id":"2007.11803","title":"MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution","created":"2020-07-23","cited_by_count":89},{"id":"2503.09641","title":"SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation","created":"2025-03-12","cited_by_count":87},{"id":"2405.18407","title":"Phased Consistency Models","created":"2024-05-28","cited_by_count":85},{"id":"2312.03816","title":"AVID: Any-Length Video Inpainting with Diffusion Model","created":"2023-12-06","cited_by_count":82},{"id":"2312.17681","title":"FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis","created":"2023-12-29","cited_by_count":79},{"id":"2010.11838","title":"Blind Video Temporal Consistency via Deep Video Prior","created":"2020-10-22","cited_by_count":76},{"id":"2411.16375","title":"Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing","created":"2024-11-25","cited_by_count":74},{"id":"2207.08494","title":"Rethinking Alignment in Video Super-Resolution Transformers","created":"2022-07-18","cited_by_count":71},{"id":"1909.08080","title":"Efficient Video Super-Resolution through Recurrent Latent Space Propagation","created":"2019-09-17","cited_by_count":69},{"id":"2410.08151","title":"Progressive Autoregressive Video Diffusion Models","created":"2024-10-10","cited_by_count":68},{"id":"2403.06807","title":"Multistep Consistency Models","created":"2024-03-11","cited_by_count":67},{"id":"2407.07667","title":"VEnhancer: Generative Space-Time Enhancement for Video Generation","created":"2024-07-10","cited_by_count":64},{"id":"2405.16852","title":"EM Distillation for One-step Diffusion Models","created":"2024-05-27","cited_by_count":62},{"id":"2410.07303","title":"Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow","created":"2024-10-09","cited_by_count":61},{"id":"2503.07418","title":"AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion","created":"2025-03-10","cited_by_count":55},{"id":"2411.13383","title":"Adversarial Diffusion Compression for Real-World Image Super-Resolution","created":"2024-11-20","cited_by_count":52},{"id":"2403.12962","title":"FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation","created":"2024-03-19","cited_by_count":49},{"id":"2410.20280","title":"MarDini: Masked Autoregressive Diffusion for Video Generation at Scale","created":"2024-10-26","cited_by_count":49},{"id":"2203.14206","title":"Denoising Likelihood Score Matching for Conditional Score-based Data Generation","created":"2022-03-27","cited_by_count":46},{"id":"2003.04716","title":"Deep Blind Video Super-resolution","created":"2020-03-10","cited_by_count":44},{"id":"2406.06890","title":"Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation","created":"2024-06-11","cited_by_count":39},{"id":"2305.00163","title":"Enhancing Video Super-Resolution via Implicit Resampling-based Alignment","created":"2023-04-29","cited_by_count":35},{"id":"2407.13987","title":"RealViformer: Investigating Attention for Real-World Video Super-Resolution","created":"2024-07-19","cited_by_count":33},{"id":"2008.09370","title":"Learning Camera-Aware Noise Models","created":"2020-08-21","cited_by_count":32},{"id":"2409.19681","title":"Simple and Fast Distillation of Diffusion Models","created":"2024-09-29","cited_by_count":32},{"id":"2404.12388","title":"VideoGigaGAN: Towards Detail-rich Video Super-Resolution","created":"2024-04-18","cited_by_count":30},{"id":"2401.06312","title":"Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention","created":"2024-01-12","cited_by_count":29},{"id":"2401.03707","title":"FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring","created":"2024-01-08","cited_by_count":28},{"id":"2308.07314","title":"Dual Associated Encoder for Face Restoration","created":"2023-08-14","cited_by_count":23},{"id":"2305.18107","title":"Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution","created":"2023-05-29","cited_by_count":21},{"id":"2008.04902","title":"Learning to See Through Obstructions with Layered Decomposition","created":"2020-08-11","cited_by_count":20},{"id":"2308.10079","title":"MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance","created":"2023-08-19","cited_by_count":17},{"id":"2407.08680","title":"Generalizable Implicit Motion Modeling for Video Frame Interpolation","created":"2024-07-11","cited_by_count":16},{"id":"2206.07687","title":"Structured Sparsity Learning for Efficient Video Super-Resolution","created":"2022-06-15","cited_by_count":14},{"id":"2410.03936","title":"Learning Truncated Causal History Model for Video Restoration","created":"2024-10-04","cited_by_count":13},{"id":"2305.02660","title":"Expanding Synthetic Real-World Degradations for Blind Video Super Resolution","created":"2023-05-04","cited_by_count":12},{"id":"2510.15868","title":"LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal","created":"2025-10-17","cited_by_count":9},{"id":"2312.09909","title":"TMP: Temporal Motion Propagation for Online Video Super-Resolution","created":"2023-12-15","cited_by_count":8},{"id":"2501.02355","title":"CorrFill: Enhancing Faithfulness in Reference-based Inpainting with Correspondence Guidance in Diffusion Models","created":"2025-01-04","cited_by_count":8},{"id":"2510.00948","title":"InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution","created":"2025-10-01","cited_by_count":8},{"id":"2107.05307","title":"Real-Time Super-Resolution System of 4K-Video Based on Deep Learning","created":"2021-07-12","cited_by_count":7},{"id":"2303.00334","title":"Online Streaming Video Super-Resolution with Convolutional Look-Up Table","created":"2023-03-01","cited_by_count":7},{"id":"2409.17256","title":"AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content","created":"2024-09-25","cited_by_count":7},{"id":"2305.14669","title":"NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution","created":"2023-05-24","cited_by_count":6},{"id":"2411.13362","title":"RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content","created":"2024-11-20","cited_by_count":5}],"next_cursor":"100"}