{"list":"other","total":77,"rows":[{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1807.06521","title":"CBAM: Convolutional Block Attention Module","created":"2018-07-17","cited_by_count":2477},{"id":"1707.01083","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","created":"2017-07-04","cited_by_count":1200},{"id":"2012.00364","title":"Pre-Trained Image Processing Transformer","created":"2020-12-01","cited_by_count":893},{"id":"1808.04560","title":"Deep Retinex Decomposition for Low-Light Enhancement","created":"2018-08-14","cited_by_count":723},{"id":"2204.04676","title":"Simple Baselines for Image Restoration","created":"2022-04-10","cited_by_count":708},{"id":"1910.03151","title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","created":"2019-10-08","cited_by_count":575},{"id":"2001.06826","title":"Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement","created":"2020-01-19","cited_by_count":542},{"id":"2101.03697","title":"RepVGG: Making VGG-style ConvNets Great Again","created":"2021-01-11","cited_by_count":522},{"id":"1911.11907","title":"GhostNet: More Features from Cheap Operations","created":"2019-11-27","cited_by_count":410},{"id":"1812.01243","title":"Efficient Attention: Attention with Linear Complexities","created":"2018-12-04","cited_by_count":368},{"id":"2105.03404","title":"ResMLP: Feedforward networks for image classification with data-efficient training","created":"2021-05-07","cited_by_count":339},{"id":"2003.06792","title":"Learning Enriched Features for Real Image Restoration and Enhancement","created":"2020-03-15","cited_by_count":312},{"id":"2012.05609","title":"Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement","created":"2020-12-10","cited_by_count":303},{"id":"2303.09472","title":"DiffIR: Efficient Diffusion Model for Image Restoration","created":"2023-03-16","cited_by_count":255},{"id":"1901.05495","title":"An Underwater Image Enhancement Benchmark Dataset and Beyond","created":"2019-01-11","cited_by_count":253},{"id":"1701.03077","title":"A General and Adaptive Robust Loss Function","created":"2017-01-11","cited_by_count":236},{"id":"2303.06705","title":"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement","created":"2023-03-12","cited_by_count":231},{"id":"2103.00860","title":"Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation","created":"2021-03-01","cited_by_count":192},{"id":"1903.09766","title":"Fast Underwater Image Enhancement for Improved Visual Perception","created":"2019-03-23","cited_by_count":183},{"id":"1908.03930","title":"ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks","created":"2019-08-11","cited_by_count":178},{"id":"2212.11548","title":"Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method","created":"2022-12-22","cited_by_count":174},{"id":"2009.14468","title":"Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time","created":"2020-09-30","cited_by_count":163},{"id":"2303.03667","title":"Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks","created":"2023-03-07","cited_by_count":130},{"id":"2306.00306","title":"Low-Light Image Enhancement with Wavelet-based Diffusion Models","created":"2023-06-01","cited_by_count":127},{"id":"2307.09283","title":"RepViT: Revisiting Mobile CNN From ViT Perspective","created":"2023-07-18","cited_by_count":123},{"id":"2308.13164","title":"Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model","created":"2023-08-25","cited_by_count":118},{"id":"2206.04040","title":"MobileOne: An Improved One millisecond Mobile Backbone","created":"2022-06-08","cited_by_count":109},{"id":"2002.05509","title":"Replacing Mobile Camera ISP with a Single Deep Learning Model","created":"2020-02-13","cited_by_count":108},{"id":"2301.04805","title":"DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention","created":"2023-01-12","cited_by_count":91},{"id":"2311.16845","title":"Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration","created":"2023-11-28","cited_by_count":88},{"id":"2103.13425","title":"Diverse Branch Block: Building a Convolution as an Inception-like Unit","created":"2021-03-24","cited_by_count":87},{"id":"2205.14871","title":"You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction","created":"2022-05-30","cited_by_count":81},{"id":"1810.01109","title":"AI Benchmark: Running Deep Neural Networks on Android Smartphones","created":"2018-10-02","cited_by_count":75},{"id":"2110.00970","title":"Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement","created":"2021-10-03","cited_by_count":60},{"id":"2405.03349","title":"Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement","created":"2024-05-06","cited_by_count":59},{"id":"2404.10343","title":"The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report","created":"2024-04-16","cited_by_count":57},{"id":"2403.19967","title":"Rewrite the Stars","created":"2024-03-29","cited_by_count":55},{"id":"2207.09689","title":"Uncertainty Inspired Underwater Image Enhancement","created":"2022-07-20","cited_by_count":54},{"id":"2011.04994","title":"AIM 2020 Challenge on Learned Image Signal Processing Pipeline","created":"2020-11-10","cited_by_count":44},{"id":"2211.12905","title":"GhostNetV2: Enhance Cheap Operation with Long-Range Attention","created":"2022-11-23","cited_by_count":44},{"id":"2108.08119","title":"Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision","created":"2021-08-18","cited_by_count":43},{"id":"2305.13563","title":"Efficient Multi-Scale Attention Module with Cross-Spatial Learning","created":"2023-05-23","cited_by_count":41},{"id":"2406.00629","title":"Correlation Matching Transformation Transformers for UHD Image Restoration","created":"2024-06-02","cited_by_count":39},{"id":"2008.09228","title":"AWNet: Attentive Wavelet Network for Image ISP","created":"2020-08-20","cited_by_count":38},{"id":"2301.01146","title":"Rethinking Mobile Block for Efficient Attention-based Models","created":"2023-01-03","cited_by_count":36},{"id":"2101.02073","title":"Shallow-UWnet : Compressed Model for Underwater Image Enhancement","created":"2021-01-06","cited_by_count":35},{"id":"2105.07809","title":"Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report","created":"2021-05-17","cited_by_count":34},{"id":"2309.03445","title":"Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy","created":"2023-09-07","cited_by_count":33},{"id":"2309.00121","title":"Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation","created":"2023-08-31","cited_by_count":30},{"id":"1812.00353","title":"Accelerate CNN via Recursive Bayesian Pruning","created":"2018-12-02","cited_by_count":27},{"id":"2309.04089","title":"Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement","created":"2023-09-08","cited_by_count":27},{"id":"2404.14248","title":"NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results","created":"2024-04-22","cited_by_count":26},{"id":"2311.12770","title":"Swift Parameter-free Attention Network for Efficient Super-Resolution","created":"2023-11-21","cited_by_count":24},{"id":"2106.07910","title":"Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration","created":"2021-06-15","cited_by_count":23},{"id":"2305.12972","title":"VanillaNet: the Power of Minimalism in Deep Learning","created":"2023-05-22","cited_by_count":23},{"id":"2305.08824","title":"Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement","created":"2023-05-15","cited_by_count":22},{"id":"2205.05671","title":"RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization","created":"2022-05-11","cited_by_count":19},{"id":"2202.08758","title":"A Wavelet-based Dual-stream Network for Underwater Image Enhancement","created":"2022-02-17","cited_by_count":16},{"id":"2105.14447","title":"EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network","created":"2021-05-30","cited_by_count":15},{"id":"2401.02161","title":"Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain","created":"2024-01-04","cited_by_count":14},{"id":"2406.01028","title":"LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network","created":"2024-06-03","cited_by_count":14},{"id":"2211.03885","title":"Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report","created":"2022-11-07","cited_by_count":13},{"id":"2309.08382","title":"Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance","created":"2023-09-15","cited_by_count":12},{"id":"2109.03508","title":"RepNAS: Searching for Efficient Re-parameterizing Blocks","created":"2021-09-08","cited_by_count":11},{"id":"2404.10700","title":"Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs","created":"2024-04-16","cited_by_count":11},{"id":"2009.12021","title":"Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters","created":"2020-09-25","cited_by_count":9},{"id":"2310.10563","title":"RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets","created":"2023-10-16","cited_by_count":9},{"id":"2111.12419","title":"NAM: Normalization-based Attention Module","created":"2021-11-24","cited_by_count":8},{"id":"2305.11474","title":"Reciprocal Attention Mixing Transformer for Lightweight Image Restoration","created":"2023-05-19","cited_by_count":6},{"id":"2403.06793","title":"Boosting Image Restoration via Priors from Pre-trained Models","created":"2024-03-11","cited_by_count":6},{"id":"2308.08137","title":"SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device","created":"2023-08-16","cited_by_count":5},{"id":"2308.12494","title":"MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices","created":"2023-08-24","cited_by_count":5},{"id":"2404.13884","title":"MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs","created":"2024-04-22","cited_by_count":5},{"id":"2405.16197","title":"A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior","created":"2024-05-25","cited_by_count":2},{"id":"2107.11170","title":"Bias Loss for Mobile Neural Networks","created":"2021-07-23","cited_by_count":1}],"next_cursor":null}