{"list":"other","total":130,"rows":[{"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":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"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":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"2006.09882","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","created":"2020-06-17","cited_by_count":2857},{"id":"2109.01134","title":"Learning to Prompt for Vision-Language Models","created":"2021-09-02","cited_by_count":2281},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1807.10165","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","created":"2018-07-18","cited_by_count":1525},{"id":"1709.00029","title":"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification","created":"2017-08-31","cited_by_count":1496},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1911.04252","title":"Self-training with Noisy Student improves ImageNet classification","created":"2019-11-11","cited_by_count":1311},{"id":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1807.10221","title":"Unified Perceptual Parsing for Scene Understanding","created":"2018-07-26","cited_by_count":1271},{"id":"1608.05442","title":"Semantic Understanding of Scenes through the ADE20K Dataset","created":"2016-08-18","cited_by_count":1069},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1705.09406","title":"Multimodal Machine Learning: A Survey and Taxonomy","created":"2017-05-26","cited_by_count":1013},{"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":"2210.03117","title":"MaPLe: Multi-modal Prompt Learning","created":"2022-10-06","cited_by_count":687},{"id":"1902.03368","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","created":"2019-02-09","cited_by_count":669},{"id":"2001.05566","title":"Image Segmentation Using Deep Learning: A Survey","created":"2020-01-15","cited_by_count":660},{"id":"1706.00384","title":"Deep Mutual Learning","created":"2017-06-01","cited_by_count":628},{"id":"2106.05735","title":"The Medical Segmentation Decathlon","created":"2021-06-10","cited_by_count":628},{"id":"1911.07069","title":"Kvasir-SEG: A Segmented Polyp Dataset","created":"2019-11-16","cited_by_count":589},{"id":"1905.05055","title":"Object Detection in 20 Years: A Survey","created":"2019-05-13","cited_by_count":531},{"id":"1908.02983","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","created":"2019-08-08","cited_by_count":510},{"id":"1901.04056","title":"The Liver Tumor Segmentation Benchmark (LiTS)","created":"2019-01-13","cited_by_count":509},{"id":"2106.01226","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","created":"2021-06-02","cited_by_count":463},{"id":"2003.09005","title":"Semi-Supervised Semantic Segmentation with Cross-Consistency Training","created":"2020-03-19","cited_by_count":376},{"id":"1902.09063","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","created":"2019-02-25","cited_by_count":373},{"id":"2106.09018","title":"End-to-End Semi-Supervised Object Detection with Soft Teacher","created":"2021-06-16","cited_by_count":316},{"id":"1711.11575","title":"Relation Networks for Object Detection","created":"2017-11-30","cited_by_count":305},{"id":"2101.06329","title":"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning","created":"2021-01-15","cited_by_count":301},{"id":"2010.03978","title":"A Brief Review of Domain Adaptation","created":"2020-10-07","cited_by_count":288},{"id":"1412.4864","title":"Learning with Pseudo-Ensembles","created":"2014-12-16","cited_by_count":241},{"id":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232},{"id":"2007.07936","title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","created":"2020-07-15","cited_by_count":209},{"id":"1905.12886","title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","created":"2019-05-30","cited_by_count":197},{"id":"2106.05095","title":"ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation","created":"2021-06-09","cited_by_count":184},{"id":"2012.02951","title":"FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding","created":"2020-12-05","cited_by_count":181},{"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":"1802.07934","title":"Adversarial Learning for Semi-Supervised Semantic Segmentation","created":"2018-02-22","cited_by_count":173},{"id":"1506.06448","title":"DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation","created":"2015-06-22","cited_by_count":159},{"id":"1908.05724","title":"Semi-Supervised Semantic Segmentation with High- and Low-level Consistency","created":"2019-08-15","cited_by_count":147},{"id":"2104.05833","title":"Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization","created":"2021-04-12","cited_by_count":141},{"id":"1911.09296","title":"xBD: A Dataset for Assessing Building Damage from Satellite Imagery","created":"2019-11-21","cited_by_count":139},{"id":"2103.08896","title":"Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation","created":"2021-03-16","cited_by_count":134},{"id":"2202.06687","title":"Domain Adaptation via Prompt Learning","created":"2022-02-14","cited_by_count":128},{"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":"1807.05713","title":"Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models","created":"2018-07-16","cited_by_count":119},{"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":"2006.16806","title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","created":"2020-06-28","cited_by_count":81},{"id":"2104.04465","title":"Bootstrapping Semantic Segmentation with Regional Contrast","created":"2021-04-09","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":"2303.08600","title":"MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving","created":"2023-03-15","cited_by_count":68},{"id":"2104.00332","title":"UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training","created":"2021-04-01","cited_by_count":64},{"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":"1607.06215","title":"A Comprehensive Survey on Cross-modal Retrieval","created":"2016-07-21","cited_by_count":62},{"id":"1811.05249","title":"Modular Networks: Learning to Decompose Neural Computation","created":"2018-11-13","cited_by_count":62},{"id":"2202.07136","title":"Debiased Self-Training for Semi-Supervised Learning","created":"2022-02-15","cited_by_count":62},{"id":"1903.11233","title":"Deep Co-Training for Semi-Supervised Image Segmentation","created":"2019-03-27","cited_by_count":58},{"id":"2204.02078","title":"Semi-supervised Semantic Segmentation with Error Localization Network","created":"2022-04-05","cited_by_count":57},{"id":"2104.07861","title":"SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network","created":"2021-04-16","cited_by_count":54},{"id":"2201.08657","title":"Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation","created":"2022-01-21","cited_by_count":51},{"id":"2308.13985","title":"Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective","created":"2023-08-27","cited_by_count":51},{"id":"2106.01432","title":"SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training","created":"2021-06-02","cited_by_count":45},{"id":"2205.10334","title":"UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation","created":"2022-05-20","cited_by_count":45},{"id":"2306.04300","title":"CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation","created":"2023-06-07","cited_by_count":44},{"id":"2210.05944","title":"ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation","created":"2022-10-12","cited_by_count":42},{"id":"2106.08922","title":"Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition","created":"2021-06-16","cited_by_count":40},{"id":"2303.15014","title":"Leveraging Hidden Positives for Unsupervised Semantic Segmentation","created":"2023-03-27","cited_by_count":40},{"id":"1811.07073","title":"Semi-Supervised Semantic Image Segmentation with Self-correcting Networks","created":"2018-11-17","cited_by_count":37},{"id":"2303.01276","title":"Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation","created":"2023-03-02","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":"2305.16404","title":"GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds","created":"2023-05-25","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":"2312.03585","title":"Foundation Model Assisted Weakly Supervised Semantic Segmentation","created":"2023-12-06","cited_by_count":33},{"id":"2012.00827","title":"A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation","created":"2020-12-01","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":"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":"2307.07944","title":"Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling","created":"2023-07-16","cited_by_count":28},{"id":"2109.13930","title":"All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation","created":"2021-09-28","cited_by_count":25},{"id":"2103.11351","title":"Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving","created":"2021-03-21","cited_by_count":23}],"next_cursor":"100"}