{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/segmentation/papers/92","list_of":"/task/segmentation","task":"Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":92,"pages_in_order":131,"rows_per_page":100,"rows":[9101,9200],"of":13072,"counts":{"archive_papers_tagged":13072,"with_a_code_link":5255,"where_syntology_ran_a_sample":976,"not_listed_spam_title":0,"listed":13072,"listed_where_code_ran":976,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":838,"every_run_a_failure_of_syntologys_instrument":138,"listed_with_a_run_with_no_instrument_failure":838,"listed_every_run_a_failure_of_syntologys_instrument":138,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/segmentation","prev":"/task/segmentation/papers/91","next":"/task/segmentation/papers/93","papers":[{"url":"/paper/exploring-dual-attention-mechanism-with-multi","slug":"exploring-dual-attention-mechanism-with-multi","title":"Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism","date":"2021-11-16","arxiv_id":"2111.08708","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedcostwavg-a-new-averaging-for-better","title":"FedCostWAvg: A new averaging for better Federated Learning","date":"2021-11-16","arxiv_id":"2111.08649","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-text-for-agglutinative-and","title":"Predictive text for agglutinative and polysynthetic languages","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"research-on-the-evaluation-of-token-imbalance","title":"Research on the Evaluation of Token Imbalance Degree of NMT Corpus","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-3d-scene-segmentation-through","title":"Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion","date":"2021-11-16","arxiv_id":"2111.08434","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-discourse-parsing-as-text-to","title":"Sentence-Level Discourse Parsing as Text-to-Text Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-in-subword-tokenization-humans-vs","title":"Strategies in subword tokenization: humans vs. algorithms","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"that-slepen-al-the-nyght-with-open-ye-cross","title":"That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-chinese-word-segmentation-with","title":"Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-fire-segmentation-by","title":"Weakly-supervised fire segmentation by visualizing intermediate CNN layers","date":"2021-11-16","arxiv_id":"2111.08401","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-medical-image-segmentation-with","title":"Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration","date":"2021-11-15","arxiv_id":"2111.07716","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-automl-automated-machine-learning-for-1","title":"T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging","date":"2021-11-15","arxiv_id":"2111.07535","repositories_listed":0,"syntology":null},{"url":null,"slug":"background-aware-3d-point-cloud","title":"Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation","date":"2021-11-14","arxiv_id":"2111.07248","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-for-automatic-grain","title":"Deep Neural Networks for Automatic Grain-matrix Segmentation in Plane and Cross-polarized Sandstone Photomicrographs","date":"2021-11-13","arxiv_id":"2111.07102","repositories_listed":0,"syntology":null},{"url":null,"slug":"developing-a-novel-approach-for-periapical","title":"Developing a Novel Approach for Periapical Dental Radiographs Segmentation","date":"2021-11-13","arxiv_id":"2111.07156","repositories_listed":0,"syntology":null},{"url":null,"slug":"sci-net-a-scale-invariant-model-for-building","title":"Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery","date":"2021-11-12","arxiv_id":"2111.06812","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-medical-segmentation-with","title":"SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks","date":"2021-11-10","arxiv_id":"2111.05978","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-instance-segmentation-of-surgical","title":"Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion","date":"2021-11-09","arxiv_id":"2111.04911","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-spiking-instance-segmentation-on","title":"Unsupervised Spiking Instance Segmentation on Event Data using STDP","date":"2021-11-09","arxiv_id":"2111.05283","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-enhanced-generation-and-multi","title":"Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities","date":"2021-11-08","arxiv_id":"2111.04735","repositories_listed":0,"syntology":null},{"url":null,"slug":"limoseg-real-time-bird-s-eye-view-based-lidar","title":"LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation","date":"2021-11-08","arxiv_id":"2111.04875","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modality-cardiac-image-analysis-with","title":"Multi-Modality Cardiac Image Analysis with Deep Learning","date":"2021-11-08","arxiv_id":"2111.04736","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-multiple-myeloma-plasma-cells","title":"Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels","date":"2021-11-08","arxiv_id":"2111.05125","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-magnetic-resonance-images-for","title":"Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation","date":"2021-11-08","arxiv_id":"2111.04737","repositories_listed":0,"syntology":null},{"url":null,"slug":"acquisition-invariant-brain-mri-segmentation","title":"Acquisition-invariant brain MRI segmentation with informative uncertainties","date":"2021-11-07","arxiv_id":"2111.04094","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-segment-based-optimization-for","title":"Hierarchical Segment-based Optimization for SLAM","date":"2021-11-07","arxiv_id":"2111.04101","repositories_listed":0,"syntology":null},{"url":null,"slug":"fbnet-feature-balance-network-for-urban-scene","title":"FBNet: Feature Balance Network for Urban-Scene Segmentation","date":"2021-11-05","arxiv_id":"2111.03286","repositories_listed":0,"syntology":null},{"url":null,"slug":"hepatic-vessel-segmentation-based-on-3dswin","title":"Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention","date":"2021-11-05","arxiv_id":"2111.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-on-classification-for-fire","title":"Attention on Classification for Fire Segmentation","date":"2021-11-04","arxiv_id":"2111.03129","repositories_listed":0,"syntology":null},{"url":"/paper/lvis-challenge-track-technical-report-1st","slug":"lvis-challenge-track-technical-report-1st","title":"LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation","date":"2021-11-04","arxiv_id":"2111.02668","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-panoptic-3d-parsing-for-single-image","title":"Towards Panoptic 3D Parsing for Single Image in the Wild","date":"2021-11-04","arxiv_id":"2111.03039","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-ultrasound-vessel-segmentation-with","title":"Automatic ultrasound vessel segmentation with deep spatiotemporal context learning","date":"2021-11-03","arxiv_id":"2111.02461","repositories_listed":0,"syntology":null},{"url":"/paper/hs3-learning-with-proper-task-complexity-in","slug":"hs3-learning-with-proper-task-complexity-in","title":"HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation","date":"2021-11-03","arxiv_id":"2111.02333","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-image-compression-for-machine","title":"Learned Image Compression for Machine Perception","date":"2021-11-03","arxiv_id":"2111.02249","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pixel-level-meta-learner-for-weakly","title":"A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation","date":"2021-11-02","arxiv_id":"2111.01418","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tri-attention-fusion-guided-multi-modal","title":"A Tri-attention Fusion Guided Multi-modal Segmentation Network","date":"2021-11-02","arxiv_id":"2111.01623","repositories_listed":0,"syntology":null},{"url":null,"slug":"cpseg-cluster-free-panoptic-segmentation-of","title":"CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds","date":"2021-11-02","arxiv_id":"2111.01723","repositories_listed":0,"syntology":null},{"url":null,"slug":"detect-and-segment-a-deep-learning-approach","title":"Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation","date":"2021-11-02","arxiv_id":"2111.01590","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-language-model-based-generative-classifier","title":"A Language Model-based Generative Classifier for Sentence-level Discourse Parsing","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transformer-based-approach-towards","title":"A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-monolingual-vocabularies-to","title":"Clustering Monolingual Vocabularies to Improve Cross-Lingual Generalization","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-and-clinically-accurate-head","title":"Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study","date":"2021-11-01","arxiv_id":"2111.01544","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlation-between-image-quality-metrics-of","title":"Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy","date":"2021-11-01","arxiv_id":"2111.01093","repositories_listed":0,"syntology":null},{"url":null,"slug":"cuni-systems-in-wmt21-revisiting","title":"CUNI Systems in WMT21: Revisiting Backtranslation Techniques for English-Czech NMT","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"geodesic-models-with-convexity-shape-prior","title":"Geodesic Models with Convexity Shape Prior","date":"2021-11-01","arxiv_id":"2111.00794","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-lingual-discourse-segmentation-and","title":"Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"redundancy-reduction-in-semantic-segmentation","title":"Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs","date":"2021-11-01","arxiv_id":"2111.00742","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-mask-and-predict-augmenting-chinese","title":"Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sub-cortical-structure-segmentation-database","title":"Sub-cortical structure segmentation database for young population","date":"2021-11-01","arxiv_id":"2111.01561","repositories_listed":0,"syntology":null},{"url":null,"slug":"t4t-solution-wmt21-similar-language-task-for","title":"T4T Solution: WMT21 Similar Language Task for the Spanish-Catalan and Spanish-Portuguese Language Pair","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-coefficient-of-change-in-the-built","title":"Creating A Coefficient of Change in the Built Environment After a Natural Disaster","date":"2021-10-31","arxiv_id":"2111.04462","repositories_listed":0,"syntology":null},{"url":null,"slug":"drbanet-a-lightweight-dual-resolution-network","title":"DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary","date":"2021-10-31","arxiv_id":"2111.00509","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-boundary-uncertainty-into-loss","title":"Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation","date":"2021-10-31","arxiv_id":"2111.00533","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-debiased-and-disentangled","title":"Learning Debiased and Disentangled Representations for Semantic Segmentation","date":"2021-10-31","arxiv_id":"2111.00531","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-sampling-augment-optimal-and-efficient","title":"Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation","date":"2021-10-31","arxiv_id":"2111.00487","repositories_listed":0,"syntology":null},{"url":"/paper/mfnet-multi-class-few-shot-segmentation","slug":"mfnet-multi-class-few-shot-segmentation","title":"MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning","date":"2021-10-30","arxiv_id":"2111.00232","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-mada-unsupervised-cross-modality","title":"C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation","date":"2021-10-29","arxiv_id":"2110.15823","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-deterministic-uncertainty-for-semantic","title":"Deep Deterministic Uncertainty for Semantic Segmentation","date":"2021-10-29","arxiv_id":"2111.00079","repositories_listed":0,"syntology":null},{"url":null,"slug":"false-positive-detection-and-prediction","title":"False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation","date":"2021-10-29","arxiv_id":"2110.15681","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-and-multi-modal-learning-for-rgb","title":"Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition","date":"2021-10-29","arxiv_id":"2110.15639","repositories_listed":0,"syntology":null},{"url":null,"slug":"polyline-based-generative-navigable-space","title":"Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation","date":"2021-10-29","arxiv_id":"2111.00063","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpu-based-gmm-segmentation-of-kinect-data","title":"GPU based GMM segmentation of kinect data","date":"2021-10-28","arxiv_id":"2110.14934","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-placard-discovery-for-semantic","title":"Efficient Placard Discovery for Semantic Mapping During Frontier Exploration","date":"2021-10-27","arxiv_id":"2110.14742","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-auc-optimization-to-multiclass","title":"Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data","date":"2021-10-27","arxiv_id":"2110.14425","repositories_listed":0,"syntology":null},{"url":null,"slug":"pl-net-progressive-learning-network-for","title":"PL-Net: Progressive Learning Network for Medical Image Segmentation","date":"2021-10-27","arxiv_id":"2110.14484","repositories_listed":0,"syntology":null},{"url":null,"slug":"ta-net-topology-aware-network-for-gland","title":"TA-Net: Topology-Aware Network for Gland Segmentation","date":"2021-10-27","arxiv_id":"2110.14593","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-light-weight-interpretable","title":"A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation","date":"2021-10-26","arxiv_id":"2110.13846","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-segmentation-of-cerebral","title":"Deep Learning-based Segmentation of Cerebral Aneurysms in 3D TOF-MRA using Coarse-to-Fine Framework","date":"2021-10-26","arxiv_id":"2110.13432","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-magnification-network-for-vessel","title":"Image Magnification Network for Vessel Segmentation in OCTA Images","date":"2021-10-26","arxiv_id":"2110.13428","repositories_listed":0,"syntology":null},{"url":null,"slug":"w-net-a-two-stage-convolutional-network-for","title":"W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image","date":"2021-10-26","arxiv_id":"2110.13670","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-segmentation-of-novel-coronavirus","title":"Novel coronavirus pneumonia lesion segmentation in CT images","date":"2021-10-25","arxiv_id":"2110.12827","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-segmentation-via-deep-learning","title":"Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces","date":"2021-10-25","arxiv_id":"2110.12939","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptual-consistency-in-video-segmentation","title":"Perceptual Consistency in Video Segmentation","date":"2021-10-24","arxiv_id":"2110.12385","repositories_listed":0,"syntology":null},{"url":"/paper/x-distill-improving-self-supervised-monocular","slug":"x-distill-improving-self-supervised-monocular","title":"X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation","date":"2021-10-24","arxiv_id":"2110.12516","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-shape-guided-segmentation-network-for","title":"Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images","date":"2021-10-23","arxiv_id":"2110.12192","repositories_listed":0,"syntology":null},{"url":null,"slug":"espiownage-tracking-transients-in-steelpan","title":"espiownage: Tracking Transients in Steelpan Drum Strikes Using Surveillance Technology","date":"2021-10-23","arxiv_id":"2110.12261","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-semantic-segmentation-of-1","title":"Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations","date":"2021-10-22","arxiv_id":"2110.11998","repositories_listed":0,"syntology":null},{"url":null,"slug":"2020-cataracts-semantic-segmentation","title":"2020 CATARACTS Semantic Segmentation Challenge","date":"2021-10-21","arxiv_id":"2110.10965","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-inter-pixel-correlations-in","title":"Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation","date":"2021-10-21","arxiv_id":"2110.10916","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-tracking-and-segmentation-with-a","title":"Multi-Object Tracking and Segmentation with a Space-Time Memory Network","date":"2021-10-21","arxiv_id":"2110.11284","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-reducing-aleatoric-uncertainty-for","title":"Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks","date":"2021-10-21","arxiv_id":"2110.11012","repositories_listed":0,"syntology":null},{"url":null,"slug":"after-unet-axial-fusion-transformer-unet-for","title":"AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation","date":"2021-10-20","arxiv_id":"2110.10403","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-domain-adaptation-for-2","title":"Semi-supervised Domain Adaptation for Semantic Segmentation","date":"2021-10-20","arxiv_id":"2110.10639","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-accurate-and-reliable-iris","title":"Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning","date":"2021-10-20","arxiv_id":"2110.10334","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-transformer-for-3d-point","title":"Spatial-Temporal Transformer for 3D Point Cloud Sequences","date":"2021-10-19","arxiv_id":"2110.09783","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-framework-for-generalized-low-shot","title":"A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data","date":"2021-10-18","arxiv_id":"2110.09260","repositories_listed":0,"syntology":null},{"url":null,"slug":"color-image-segmentation-using-multi","title":"Color Image Segmentation Using Multi-Objective Swarm Optimizer and Multi-level Histogram Thresholding","date":"2021-10-18","arxiv_id":"2110.09217","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-semi-supervised-few-shot","title":"Uncertainty-Aware Semi-Supervised Few Shot Segmentation","date":"2021-10-18","arxiv_id":"2110.08954","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-w-net-improved-skip-connections-for","title":"Attention W-Net: Improved Skip Connections for better Representations","date":"2021-10-17","arxiv_id":"2110.08811","repositories_listed":0,"syntology":null},{"url":"/paper/pixel-level-intra-domain-adaptation-for","slug":"pixel-level-intra-domain-adaptation-for","title":"Pixel-level Intra-domain Adaptation for Semantic Segmentation","date":"2021-10-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-u-net-for-segmenting-flat-and","title":"Self-Supervised U-Net for Segmenting Flat and Sessile Polyps","date":"2021-10-17","arxiv_id":"2110.08776","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporally-stable-video-segmentation-without","title":"Temporally stable video segmentation without video annotations","date":"2021-10-17","arxiv_id":"2110.08893","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-classification-directly-training","title":"Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation","date":"2021-10-14","arxiv_id":"2110.07742","repositories_listed":0,"syntology":null},{"url":null,"slug":"possibilistic-fuzzy-local-information-c-means-1","title":"Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation","date":"2021-10-14","arxiv_id":"2110.07433","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-over-pre-trained-transformer-for","title":"Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence","date":"2021-10-14","arxiv_id":"2110.07160","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-teacher-student-framework-with-fourier","title":"A Teacher-Student Framework with Fourier Augmentation for COVID-19 Infection Segmentation in CT Images","date":"2021-10-13","arxiv_id":"2110.06411","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-for-3d","title":"Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data","date":"2021-10-13","arxiv_id":"2110.06632","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-political-leanings-of-chinese","title":"Prediction of Political Leanings of Chinese Speaking Twitter Users","date":"2021-10-12","arxiv_id":"2110.05723","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-semantic-segmentation-by-1","title":"Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty","date":"2021-10-12","arxiv_id":"2110.05926","repositories_listed":0,"syntology":null},{"url":null,"slug":"aweu-net-an-attention-aware-weight-excitation","title":"AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation","date":"2021-10-11","arxiv_id":"2110.05144","repositories_listed":0,"syntology":null}],"record_sha256":"6098535c4e3e9761c8abbfd6a8ff2f366954060ce9df7493dacf1535e0dcd064","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}