{"total":20,"rows":[{"id":"2206.06363","title":"Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation","created":"2022-06-13","cited_by_count":54,"area":null},{"id":"2103.04133","title":"Learning Statistical Texture for Semantic Segmentation","created":"2021-03-06","cited_by_count":51,"area":null},{"id":"2312.05391","title":"Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook","created":"2023-12-08","cited_by_count":34,"area":null},{"id":"2103.16694","title":"Geometric Unsupervised Domain Adaptation for Semantic Segmentation","created":"2021-03-30","cited_by_count":33,"area":null},{"id":"2106.06713","title":"AutoLoss: Automated Loss Function Search in Recommendations","created":"2021-06-12","cited_by_count":31,"area":null},{"id":"2103.14026","title":"AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks","created":"2021-03-25","cited_by_count":19,"area":null},{"id":"2104.08717","title":"Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses","created":"2021-04-18","cited_by_count":12,"area":null},{"id":"2302.02234","title":"Revisiting Image Deblurring with an Efficient ConvNet","created":"2023-02-04","cited_by_count":9,"area":null},{"id":"2101.03929","title":"ORDNet: Capturing Omni-Range Dependencies for Scene Parsing","created":"2021-01-11","cited_by_count":7,"area":null},{"id":"2111.14646","title":"MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation","created":"2021-11-29","cited_by_count":7,"area":null},{"id":"2407.11224","title":"Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding","created":"2024-07-15","cited_by_count":4,"area":null},{"id":"2504.06389","title":"SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation","created":"2025-04-08","cited_by_count":4,"area":null},{"id":"2304.14071","title":"Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net","created":"2023-04-27","cited_by_count":2,"area":null},{"id":"2506.15744","title":"Pixel-wise Modulated Dice Loss for Medical Image Segmentation","created":"2025-06-17","cited_by_count":2,"area":null},{"id":"2103.12270","title":"Dilated SpineNet for Semantic Segmentation","created":"2021-03-23","cited_by_count":1,"area":null},{"id":"2502.02756","title":"Adaptive Voxel-Weighted Loss Using L1 Norms in Deep Neural Networks for Detection and Segmentation of Prostate Cancer Lesions in PET/CT Images","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2308.00947","title":"Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images","created":"2023-08-02","cited_by_count":0,"area":null},{"id":"2403.15194","title":"Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion","created":"2024-03-22","cited_by_count":0,"area":null},{"id":"2603.05202","title":"Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2604.02031","title":"Rare-Aware Autoencoding: Reconstructing Spatially Imbalanced Data","created":"2026-04-02","cited_by_count":0,"area":null}],"next_cursor":null}