{"list":"other","total":106,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"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":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"2011.10566","title":"Exploring Simple Siamese Representation Learning","created":"2020-11-20","cited_by_count":2866},{"id":"2408.00714","title":"SAM 2: Segment Anything in Images and Videos","created":"2024-08-01","cited_by_count":2863},{"id":"2006.09882","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","created":"2020-06-17","cited_by_count":2857},{"id":"1612.01105","title":"Pyramid Scene Parsing Network","created":"2016-12-04","cited_by_count":2479},{"id":"2106.08254","title":"BEiT: BERT Pre-Training of Image Transformers","created":"2021-06-15","cited_by_count":2375},{"id":"1610.02357","title":"Xception: Deep Learning with Depthwise Separable Convolutions","created":"2016-10-07","cited_by_count":2332},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"2406.09414","title":"Depth Anything V2","created":"2024-06-13","cited_by_count":1422},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"2401.10891","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","created":"2024-01-19","cited_by_count":1360},{"id":"1911.04252","title":"Self-training with Noisy Student improves ImageNet classification","created":"2019-11-11","cited_by_count":1311},{"id":"2212.07143","title":"Reproducible scaling laws for contrastive language-image learning","created":"2022-12-14","cited_by_count":1173},{"id":"2107.06278","title":"Per-Pixel Classification is Not All You Need for Semantic Segmentation","created":"2021-07-13","cited_by_count":1152},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1704.03976","title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","created":"2017-04-13","cited_by_count":920},{"id":"2111.07832","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","created":"2021-11-15","cited_by_count":903},{"id":"2303.15389","title":"EVA-CLIP: Improved Training Techniques for CLIP at Scale","created":"2023-03-27","cited_by_count":798},{"id":"2211.07636","title":"EVA: Exploring the Limits of Masked Visual Representation Learning at Scale","created":"2022-11-14","cited_by_count":691},{"id":"2210.11610","title":"Large Language Models Can Self-Improve","created":"2022-10-20","cited_by_count":664},{"id":"1706.00384","title":"Deep Mutual Learning","created":"2017-06-01","cited_by_count":628},{"id":"1811.12833","title":"ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation","created":"2018-11-30","cited_by_count":623},{"id":"1604.03540","title":"Training Region-based Object Detectors with Online Hard Example Mining","created":"2016-04-12","cited_by_count":579},{"id":"2110.08263","title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","created":"2021-10-15","cited_by_count":540},{"id":"2101.03697","title":"RepVGG: Making VGG-style ConvNets Great Again","created":"2021-01-11","cited_by_count":522},{"id":"2106.01226","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","created":"2021-06-02","cited_by_count":463},{"id":"1907.07034","title":"Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation","created":"2019-07-16","cited_by_count":379},{"id":"2003.09005","title":"Semi-Supervised Semantic Segmentation with Cross-Consistency Training","created":"2020-03-19","cited_by_count":376},{"id":"2003.10580","title":"Meta Pseudo Labels","created":"2020-03-23","cited_by_count":374},{"id":"2006.06882","title":"Rethinking Pre-training and Self-training","created":"2020-06-11","cited_by_count":352},{"id":"1911.09785","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","created":"2019-11-21","cited_by_count":349},{"id":"2106.09018","title":"End-to-End Semi-Supervised Object Detection with Soft Teacher","created":"2021-06-16","cited_by_count":316},{"id":"2102.09480","title":"Unbiased Teacher for Semi-Supervised Object Detection","created":"2021-02-18","cited_by_count":306},{"id":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232},{"id":"2203.03884","title":"Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels","created":"2022-03-08","cited_by_count":218},{"id":"2106.05095","title":"ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation","created":"2021-06-09","cited_by_count":184},{"id":"2010.09713","title":"PseudoSeg: Designing Pseudo Labels for Semantic Segmentation","created":"2020-10-19","cited_by_count":180},{"id":"2208.09910","title":"Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation","created":"2022-08-21","cited_by_count":178},{"id":"2011.11183","title":"CoMatch: Semi-supervised Learning with Contrastive Graph Regularization","created":"2020-11-23","cited_by_count":167},{"id":"2303.08810","title":"BiFormer: Vision Transformer with Bi-Level Routing Attention","created":"2023-03-15","cited_by_count":151},{"id":"1803.05984","title":"Deep Co-Training for Semi-Supervised Image Recognition","created":"2018-03-15","cited_by_count":147},{"id":"1908.05724","title":"Semi-Supervised Semantic Segmentation with High- and Low-level Consistency","created":"2019-08-15","cited_by_count":147},{"id":"2104.13415","title":"Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank","created":"2021-04-27","cited_by_count":125},{"id":"2008.05258","title":"Guided Collaborative Training for Pixel-wise Semi-Supervised Learning","created":"2020-08-12","cited_by_count":120},{"id":"2203.06915","title":"SimMatch: Semi-supervised Learning with Similarity Matching","created":"2022-03-14","cited_by_count":118},{"id":"2106.10456","title":"Humble Teachers Teach Better Students for Semi-Supervised Object Detection","created":"2021-06-19","cited_by_count":116},{"id":"2305.00673","title":"Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation","created":"2023-05-01","cited_by_count":116},{"id":"2111.12903","title":"Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation","created":"2021-11-25","cited_by_count":113},{"id":"2106.14133","title":"Semi-supervised Semantic Segmentation with Directional Context-aware Consistency","created":"2021-06-27","cited_by_count":112},{"id":"2108.09025","title":"Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation","created":"2021-08-20","cited_by_count":101},{"id":"2110.05474","title":"Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning","created":"2021-10-11","cited_by_count":101},{"id":"2104.04465","title":"Bootstrapping Semantic Segmentation with Regional Contrast","created":"2021-04-09","cited_by_count":80},{"id":"2105.08128","title":"PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training","created":"2021-05-17","cited_by_count":80},{"id":"2104.07256","title":"A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation","created":"2021-04-15","cited_by_count":79},{"id":"2107.11279","title":"Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation","created":"2021-07-23","cited_by_count":78},{"id":"2004.08514","title":"DMT: Dynamic Mutual Training for Semi-Supervised Learning","created":"2020-04-18","cited_by_count":77},{"id":"2212.04976","title":"Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation","created":"2022-12-09","cited_by_count":64},{"id":"2311.17055","title":"No Representation Rules Them All in Category Discovery","created":"2023-11-28","cited_by_count":61},{"id":"2007.08505","title":"FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning","created":"2020-07-16","cited_by_count":57},{"id":"2204.02078","title":"Semi-supervised Semantic Segmentation with Error Localization Network","created":"2022-04-05","cited_by_count":57},{"id":"2210.04388","title":"Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization","created":"2022-10-10","cited_by_count":50},{"id":"2311.16241","title":"SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance","created":"2023-11-27","cited_by_count":41},{"id":"2403.01818","title":"AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation","created":"2024-03-04","cited_by_count":35},{"id":"2301.07340","title":"Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant","created":"2023-01-18","cited_by_count":34},{"id":"2203.10026","title":"Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation","created":"2022-03-18","cited_by_count":33},{"id":"2204.08454","title":"Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images","created":"2022-04-18","cited_by_count":30},{"id":"2211.11335","title":"Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation","created":"2022-11-21","cited_by_count":30},{"id":"2405.00378","title":"Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation","created":"2024-05-01","cited_by_count":30},{"id":"2112.09690","title":"Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition","created":"2021-12-17","cited_by_count":29},{"id":"2308.12595","title":"Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation","created":"2023-08-24","cited_by_count":29},{"id":"2111.13241","title":"Learning from Temporal Gradient for Semi-supervised Action Recognition","created":"2021-11-25","cited_by_count":26},{"id":"2308.09281","title":"Diverse Cotraining Makes Strong Semi-Supervised Segmentor","created":"2023-08-18","cited_by_count":26},{"id":"2308.13168","title":"IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization","created":"2023-08-25","cited_by_count":23},{"id":"2311.17325","title":"Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation","created":"2023-11-29","cited_by_count":22},{"id":"2405.11756","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","created":"2024-05-20","cited_by_count":19},{"id":"2308.06777","title":"Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning","created":"2023-08-13","cited_by_count":17},{"id":"2403.06462","title":"Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation","created":"2024-03-11","cited_by_count":16},{"id":"2106.00609","title":"Robust Mutual Learning for Semi-supervised Semantic Segmentation","created":"2021-06-01","cited_by_count":13}],"next_cursor":"100"}