{"total":36,"rows":[{"id":"2112.01071","title":"Extract Free Dense Labels from CLIP","created":"2021-12-02","cited_by_count":583,"area":null},{"id":"2106.01226","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","created":"2021-06-02","cited_by_count":463,"area":null},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251,"area":null},{"id":"2212.04976","title":"Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation","created":"2022-12-09","cited_by_count":64,"area":null},{"id":"2208.11296","title":"Semi-Supervised and Unsupervised Deep Visual Learning: A Survey","created":"2022-08-24","cited_by_count":52,"area":null},{"id":"2201.08657","title":"Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation","created":"2022-01-21","cited_by_count":51,"area":null},{"id":"2104.13243","title":"Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation","created":"2021-04-27","cited_by_count":37,"area":null},{"id":"2303.01276","title":"Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation","created":"2023-03-02","cited_by_count":35,"area":null},{"id":"2203.10026","title":"Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation","created":"2022-03-18","cited_by_count":33,"area":null},{"id":"2211.11335","title":"Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation","created":"2022-11-21","cited_by_count":30,"area":null},{"id":"2210.08519","title":"Fuzzy Positive Learning for Semi-supervised Semantic Segmentation","created":"2022-10-16","cited_by_count":19,"area":null},{"id":"2103.14242","title":"Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation","created":"2021-03-26","cited_by_count":16,"area":null},{"id":"2209.02178","title":"Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students","created":"2022-09-06","cited_by_count":16,"area":null},{"id":"2211.16701","title":"Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation","created":"2022-11-30","cited_by_count":15,"area":null},{"id":"2208.06449","title":"When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation","created":"2022-08-12","cited_by_count":13,"area":null},{"id":"2403.01909","title":"Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey","created":"2024-03-04","cited_by_count":12,"area":null},{"id":"2209.01327","title":"Semi-Supervised Semantic Segmentation with Cross Teacher Training","created":"2022-09-03","cited_by_count":11,"area":null},{"id":"2203.05956","title":"Label-efficient Hybrid-supervised Learning for Medical Image Segmentation","created":"2022-03-10","cited_by_count":10,"area":null},{"id":"2207.07059","title":"Semi-Supervised Temporal Action Detection with Proposal-Free Masking","created":"2022-07-14","cited_by_count":10,"area":null},{"id":"2203.02792","title":"Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation","created":"2022-03-05","cited_by_count":9,"area":null},{"id":"2503.17914","title":"Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning","created":"2025-03-23","cited_by_count":8,"area":null},{"id":"2110.01519","title":"Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary","created":"2021-10-04","cited_by_count":7,"area":null},{"id":"2307.08988","title":"EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation","created":"2023-07-18","cited_by_count":6,"area":null},{"id":"2201.05121","title":"STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization","created":"2022-01-13","cited_by_count":2,"area":null},{"id":"2205.03525","title":"Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm","created":"2022-05-07","cited_by_count":2,"area":null},{"id":"2209.08172","title":"Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss","created":"2022-09-16","cited_by_count":2,"area":null},{"id":"2301.07634","title":"Training Semantic Segmentation on Heterogeneous Datasets","created":"2023-01-18","cited_by_count":2,"area":null},{"id":"2401.11738","title":"MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation","created":"2024-01-22","cited_by_count":2,"area":null},{"id":"2407.12630","title":"Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation","created":"2024-07-17","cited_by_count":2,"area":null},{"id":"2104.08732","title":"Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review","created":"2021-04-18","cited_by_count":1,"area":null},{"id":"2108.03300","title":"Medical image segmentation with imperfect 3D bounding boxes","created":"2021-08-06","cited_by_count":1,"area":null},{"id":"2203.06920","title":"DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network","created":"2022-03-14","cited_by_count":1,"area":null},{"id":"2403.09315","title":"Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations","created":"2024-03-14","cited_by_count":1,"area":null},{"id":"2303.06857","title":"An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain","created":"2023-03-13","cited_by_count":0,"area":null},{"id":"2508.10383","title":"Unlocking Robust Semantic Segmentation Performance via Label-only Elastic Deformations against Implicit Label Noise","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2610.20412","title":"Hybrid supervised instance segmentation by learning label noise suppression","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}