{"list":"other","total":51,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"2205.01917","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","created":"2022-05-04","cited_by_count":1274},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1809.00219","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","created":"2018-09-01","cited_by_count":1254},{"id":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"2108.05997","title":"MUSIQ: Multi-scale Image Quality Transformer","created":"2021-08-12","cited_by_count":1081},{"id":"2101.01169","title":"Transformers in Vision: A Survey","created":"2021-01-04","cited_by_count":1013},{"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":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"2103.00112","title":"Transformer in Transformer","created":"2021-02-27","cited_by_count":633},{"id":"2103.14006","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","created":"2021-03-25","cited_by_count":529},{"id":"2205.04437","title":"Activating More Pixels in Image Super-Resolution Transformer","created":"2022-05-09","cited_by_count":438},{"id":"1608.00367","title":"Accelerating the Super-Resolution Convolutional Neural Network","created":"2016-08-01","cited_by_count":394},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"2203.16220","title":"Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection","created":"2022-03-30","cited_by_count":317},{"id":"2209.02646","title":"A Survey on Generative Diffusion Model","created":"2022-09-06","cited_by_count":240},{"id":"2203.11926","title":"Focal Modulation Networks","created":"2022-03-22","cited_by_count":186},{"id":"1903.09814","title":"Feedback Network for Image Super-Resolution","created":"2019-03-23","cited_by_count":155},{"id":"2003.13081","title":"Structure-Preserving Super Resolution with Gradient Guidance","created":"2020-03-29","cited_by_count":116},{"id":"2107.04589","title":"ViTGAN: Training GANs with Vision Transformers","created":"2021-07-09","cited_by_count":110},{"id":"2205.12524","title":"Accelerating Diffusion Models via Early Stop of the Diffusion Process","created":"2022-05-25","cited_by_count":100},{"id":"2107.03055","title":"Blind Image Super-Resolution: A Survey and Beyond","created":"2021-07-07","cited_by_count":93},{"id":"2205.14141","title":"Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation","created":"2022-05-27","cited_by_count":93},{"id":"2103.02368","title":"Real-World Single Image Super-Resolution: A Brief Review","created":"2021-03-03","cited_by_count":65},{"id":"2110.10394","title":"Deep Learning for HDR Imaging: State-of-the-Art and Future Trends","created":"2021-10-20","cited_by_count":45},{"id":"2205.15175","title":"ShuffleMixer: An Efficient ConvNet for Image Super-Resolution","created":"2022-05-30","cited_by_count":45},{"id":"2111.15121","title":"Pyramid Adversarial Training Improves ViT Performance","created":"2021-11-30","cited_by_count":39},{"id":"2203.04962","title":"Learning the Degradation Distribution for Blind Image Super-Resolution","created":"2022-03-09","cited_by_count":27},{"id":"2205.04910","title":"A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds","created":"2022-05-10","cited_by_count":22},{"id":"2210.00752","title":"From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution","created":"2022-10-03","cited_by_count":15},{"id":"2109.03444","title":"Toward Real-World Super-Resolution via Adaptive Downsampling Models","created":"2021-09-08","cited_by_count":14},{"id":"2001.07575","title":"Multimodal Deep Unfolding for Guided Image Super-Resolution","created":"2020-01-21","cited_by_count":13},{"id":"1709.08680","title":"Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries","created":"2017-09-25","cited_by_count":8},{"id":"1812.09276","title":"Multimodal Sensor Fusion In Single Thermal image Super-Resolution","created":"2018-12-21","cited_by_count":8},{"id":"1912.05281","title":"Vine disease detection in UAV multispectral images with deep learning segmentation approach","created":"2019-12-11","cited_by_count":8},{"id":"2208.13436","title":"Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution","created":"2022-08-29","cited_by_count":8},{"id":"2109.00960","title":"Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN","created":"2021-09-02","cited_by_count":7},{"id":"2203.08581","title":"A Survey on Infrared Image and Video Sets","created":"2022-03-16","cited_by_count":7},{"id":"2207.00943","title":"Degradation-Guided Meta-Restoration Network for Blind Super-Resolution","created":"2022-07-03","cited_by_count":7},{"id":"2201.10747","title":"Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution","created":"2022-01-26","cited_by_count":6},{"id":"2208.03008","title":"Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN","created":"2022-08-05","cited_by_count":4},{"id":"1910.08320","title":"Multimodal Image Super-resolution via Deep Unfolding with Side Information","created":"2019-10-18","cited_by_count":2}],"next_cursor":null}