{"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/83","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":83,"pages_in_order":131,"rows_per_page":100,"rows":[8201,8300],"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/82","next":"/task/segmentation/papers/84","papers":[{"url":null,"slug":"mr-nom-multi-scale-resolution-of-neuronal","title":"MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging","date":"2022-11-14","arxiv_id":"2211.07415","repositories_listed":0,"syntology":null},{"url":null,"slug":"wsc-trans-a-3d-network-model-for-automatic","title":"WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT","date":"2022-11-14","arxiv_id":"2211.07143","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-semantic-segmentation-based-on-few","title":"Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview","date":"2022-11-13","arxiv_id":"2211.08352","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-benchmark-for-out-of-distribution-detection","title":"A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation","date":"2022-11-11","arxiv_id":"2211.06241","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-her2-tumor-segmentation-with-subtype","title":"Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks","date":"2022-11-11","arxiv_id":"2211.06150","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-deep-learning-for-improved-myocardial","title":"Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI","date":"2022-11-11","arxiv_id":"2211.06247","repositories_listed":0,"syntology":null},{"url":null,"slug":"driver-maneuver-detection-and-analysis-using","title":"Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification","date":"2022-11-10","arxiv_id":"2211.06463","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-analysis-regarding-the-influence","title":"Experimental analysis regarding the influence of iris segmentation on the recognition rate","date":"2022-11-10","arxiv_id":"2211.05507","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-uncertainty-based-out-of","title":"Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation","date":"2022-11-10","arxiv_id":"2211.05421","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-global-perturbation-attacks-for","title":"Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models","date":"2022-11-09","arxiv_id":"2211.04926","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-kinetic-approach-to-consensus-based","title":"A kinetic approach to consensus-based segmentation of biomedical images","date":"2022-11-08","arxiv_id":"2211.05226","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-error-detection-in-integrated","title":"Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach","date":"2022-11-08","arxiv_id":"2211.03927","repositories_listed":0,"syntology":null},{"url":null,"slug":"depthformer-multimodal-positional-encodings","title":"DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks","date":"2022-11-08","arxiv_id":"2211.04188","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-and-experimental-1","title":"Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty","date":"2022-11-08","arxiv_id":"2211.04161","repositories_listed":0,"syntology":null},{"url":"/paper/polite-teacher-semi-supervised-instance","slug":"polite-teacher-semi-supervised-instance","title":"Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding","date":"2022-11-07","arxiv_id":"2211.03850","repositories_listed":0,"syntology":null},{"url":null,"slug":"myops-net-myocardial-pathology-segmentation","title":"MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images","date":"2022-11-06","arxiv_id":"2211.03062","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-novel-mask-rcnn-architectures-for","title":"Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation","date":"2022-11-05","arxiv_id":"2211.02799","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizability-of-deep-adult-lung","title":"Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study","date":"2022-11-04","arxiv_id":"2211.02475","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-boundary-detection-for","title":"High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss","date":"2022-11-04","arxiv_id":"2211.02419","repositories_listed":0,"syntology":null},{"url":null,"slug":"isa-net-improved-spatial-attention-network","title":"ISA-Net: Improved spatial attention network for PET-CT tumor segmentation","date":"2022-11-04","arxiv_id":"2211.02256","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-the-effectiveness-of-a-generative","title":"Analysing the effectiveness of a generative model for semi-supervised medical image segmentation","date":"2022-11-03","arxiv_id":"2211.01886","repositories_listed":0,"syntology":null},{"url":null,"slug":"imagecas-a-large-scale-dataset-and-benchmark","title":"ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images","date":"2022-11-03","arxiv_id":"2211.01607","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-chinese-word-segmentation-and-span","title":"Joint Chinese Word Segmentation and Span-based Constituency Parsing","date":"2022-11-03","arxiv_id":"2211.01638","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-model-uncertainty-for-semantic","title":"Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS","date":"2022-11-03","arxiv_id":"2211.01999","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-u-net-network-for-efficient-brain-tumor","title":"Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images","date":"2022-11-03","arxiv_id":"2211.01885","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-joint-framework-towards-class-aware-and","title":"A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation","date":"2022-11-02","arxiv_id":"2211.01310","repositories_listed":0,"syntology":null},{"url":null,"slug":"circlesnake-instance-segmentation-with-circle","title":"CircleSnake: Instance Segmentation with Circle Representation","date":"2022-11-02","arxiv_id":"2211.01254","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-computer-vision-algorithms-for","title":"Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing","date":"2022-11-02","arxiv_id":"2211.01037","repositories_listed":0,"syntology":null},{"url":null,"slug":"distill-and-collect-for-semi-supervised","title":"Distill and Collect for Semi-Supervised Temporal Action Segmentation","date":"2022-11-02","arxiv_id":"2211.01311","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-disentangled-multimodal-prior","title":"Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI","date":"2022-11-02","arxiv_id":"2211.01353","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypergraph-convolutional-network-based-weakly","title":"Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations","date":"2022-11-02","arxiv_id":"2211.01174","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-augmentation-improves-medical-image","title":"Style Augmentation improves Medical Image Segmentation","date":"2022-11-02","arxiv_id":"2211.01125","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-model-adaptation-for-source-free","title":"Unsupervised Model Adaptation for Source-free Segmentation of Medical Images","date":"2022-11-02","arxiv_id":"2211.00807","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-structure-wise-uncertainty-for-3d","title":"Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation","date":"2022-11-01","arxiv_id":"2211.00303","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-melanocytic-cell-masks-from-adjacent","title":"Learning Melanocytic Cell Masks from Adjacent Stained Tissue","date":"2022-11-01","arxiv_id":"2211.00646","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifaceted-assessments-of-traditional","title":"Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seg-struct-the-interplay-between-part","title":"Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing","date":"2022-11-01","arxiv_id":"2211.00382","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-can-be-all-you-need-in-contrastive","title":"Saliency Can Be All You Need In Contrastive Self-Supervised Learning","date":"2022-10-30","arxiv_id":"2210.16776","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-regularized-prototypical-network-for-few","title":"Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation","date":"2022-10-30","arxiv_id":"2210.16829","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-sub-component-level-segmentation-and","title":"Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery","date":"2022-10-29","arxiv_id":"2210.16453","repositories_listed":0,"syntology":null},{"url":null,"slug":"tformer-3d-tooth-segmentation-in-mesh-scans","title":"TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer","date":"2022-10-29","arxiv_id":"2210.16627","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-analysis-of-diabetic-retinopathy","title":"Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias","date":"2022-10-28","arxiv_id":"2210.16053","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyper-connected-transformer-network-for-co","title":"Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation","date":"2022-10-28","arxiv_id":"2210.15808","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-diffusion-models-via-pre","title":"Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation","date":"2022-10-27","arxiv_id":"2210.17408","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-segmentation-based-hepatic","title":"Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study","date":"2022-10-27","arxiv_id":"2210.15149","repositories_listed":0,"syntology":null},{"url":null,"slug":"trscore-a-novel-gpt-based-readability-scorer","title":"TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection","date":"2022-10-27","arxiv_id":"2210.15104","repositories_listed":0,"syntology":null},{"url":null,"slug":"unet-2022-exploring-dynamics-in-non","title":"UNet-2022: Exploring Dynamics in Non-isomorphic Architecture","date":"2022-10-27","arxiv_id":"2210.15566","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-precise-are-performance-estimates-for","title":"How precise are performance estimates for typical medical image segmentation tasks?","date":"2022-10-26","arxiv_id":"2210.14677","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-bruch-s-membrane-in-retinal","title":"Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification","date":"2022-10-26","arxiv_id":"2210.14799","repositories_listed":0,"syntology":null},{"url":null,"slug":"sinco-a-novel-structural-regularizer-for","title":"SINCO: A Novel structural regularizer for image compression using implicit neural representations","date":"2022-10-26","arxiv_id":"2210.14974","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-speech-segmentation-using-acousto","title":"Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead","date":"2022-10-26","arxiv_id":"2210.14446","repositories_listed":0,"syntology":null},{"url":"/paper/from-colouring-in-to-pointillism-revisiting","slug":"from-colouring-in-to-pointillism-revisiting","title":"From colouring-in to pointillism: revisiting semantic segmentation supervision","date":"2022-10-25","arxiv_id":"2210.14142","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-explicit-object-centric","title":"Learning Explicit Object-Centric Representations with Vision Transformers","date":"2022-10-25","arxiv_id":"2210.14139","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-theory-of-stable-market-segmentations","title":"A Theory of Stable Market Segmentations","date":"2022-10-24","arxiv_id":"2210.13194","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradmix-for-nuclei-segmentation-and","title":"GradMix for nuclei segmentation and classification in imbalanced pathology image datasets","date":"2022-10-24","arxiv_id":"2210.12938","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-batch-and-patch-size-training-for","title":"Large Batch and Patch Size Training for Medical Image Segmentation","date":"2022-10-24","arxiv_id":"2210.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-efficient-iris-recognition-system","title":"Towards an efficient Iris Recognition System on Embedded Devices","date":"2022-10-24","arxiv_id":"2210.13101","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-deep-semantic-segmentation","title":"An Interpretable Deep Semantic Segmentation Method for Earth Observation","date":"2022-10-23","arxiv_id":"2210.12820","repositories_listed":0,"syntology":null},{"url":"/paper/multi-scale-patch-based-representation","slug":"multi-scale-patch-based-representation","title":"Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation","date":"2022-10-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-promoting-ensemble-for-medical","title":"Diversity-Promoting Ensemble for Medical Image Segmentation","date":"2022-10-22","arxiv_id":"2210.12388","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-dc-unext-an-mlp-based-multi-scale-feature","title":"MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images","date":"2022-10-22","arxiv_id":"2210.12361","repositories_listed":0,"syntology":null},{"url":null,"slug":"slam-semantic-learning-based-activation-map","title":"SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation","date":"2022-10-22","arxiv_id":"2210.12417","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-transformer-for-repairing-human","title":"Adversarial Transformer for Repairing Human Airway Segmentation","date":"2022-10-21","arxiv_id":"2210.12029","repositories_listed":0,"syntology":null},{"url":null,"slug":"cobnet-cross-attention-on-object-and","title":"CobNet: Cross Attention on Object and Background for Few-Shot Segmentation","date":"2022-10-21","arxiv_id":"2210.11968","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-fidelity-visual-structural-inspections","title":"High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers","date":"2022-10-21","arxiv_id":"2210.12175","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-image-semantic-segmentation","slug":"unsupervised-image-semantic-segmentation","title":"Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks","date":"2022-10-21","arxiv_id":"2210.11810","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-segmentation-of-breast-ultrasound","title":"Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique","date":"2022-10-20","arxiv_id":"2210.10975","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgtunet-an-new-unet-for-colon-nuclei-instance","title":"MGTUNet: An new UNet for colon nuclei instance segmentation and quantification","date":"2022-10-20","arxiv_id":"2210.10981","repositories_listed":0,"syntology":null},{"url":null,"slug":"rais-robust-and-accurate-interactive","title":"RAIS: Robust and Accurate Interactive Segmentation via Continual Learning","date":"2022-10-20","arxiv_id":"2210.10984","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferring-learned-patterns-from-ground","title":"Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming","date":"2022-10-20","arxiv_id":"2210.11545","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-deep-learning-1","title":"Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation","date":"2022-10-19","arxiv_id":"2210.10641","repositories_listed":0,"syntology":null},{"url":null,"slug":"havana-hard-negative-samples-aware-self","title":"HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation","date":"2022-10-19","arxiv_id":"2210.10626","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-lung-segmentation-based-on-u-net","title":"Improved lung segmentation based on U-Net architecture and morphological operations","date":"2022-10-19","arxiv_id":"2210.10545","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-free-direct-iris-localization","title":"Segmentation-free Direct Iris Localization Networks","date":"2022-10-19","arxiv_id":"2210.10403","repositories_listed":0,"syntology":null},{"url":"/paper/number-adaptive-prototype-learning-for-3d","slug":"number-adaptive-prototype-learning-for-3d","title":"Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation","date":"2022-10-18","arxiv_id":"2210.09948","repositories_listed":0,"syntology":null},{"url":null,"slug":"otsu-based-differential-evolution-method-for","title":"Otsu based Differential Evolution Method for Image Segmentation","date":"2022-10-18","arxiv_id":"2210.10005","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-multi-modal-semantic-fusion-on-1","title":"Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation","date":"2022-10-18","arxiv_id":"2210.09739","repositories_listed":0,"syntology":null},{"url":null,"slug":"cerebrovascular-segmentation-via-vessel","title":"Cerebrovascular Segmentation via Vessel Oriented Filtering Network","date":"2022-10-17","arxiv_id":"2210.08868","repositories_listed":0,"syntology":null},{"url":null,"slug":"cutting-splicing-data-augmentation-a-novel","title":"Cutting-Splicing data augmentation: A novel technology for medical image segmentation","date":"2022-10-17","arxiv_id":"2210.09099","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-feature-distillation-network","title":"Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation","date":"2022-10-17","arxiv_id":"2210.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-with-active-semi-1","title":"Semantic Segmentation with Active Semi-Supervised Representation Learning","date":"2022-10-16","arxiv_id":"2210.08403","repositories_listed":0,"syntology":null},{"url":null,"slug":"mkis-net-a-light-weight-multi-kernel-network","title":"MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation","date":"2022-10-15","arxiv_id":"2210.08168","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-limited-tissue-segmentation-using","title":"Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning","date":"2022-10-14","arxiv_id":"2210.07936","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-segmentation-with-cross-modal","title":"Instance Segmentation with Cross-Modal Consistency","date":"2022-10-14","arxiv_id":"2210.08113","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-label-efficient-semantic-segmentation","title":"LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds","date":"2022-10-14","arxiv_id":"2210.08064","repositories_listed":0,"syntology":null},{"url":null,"slug":"monodvps-a-self-supervised-monocular-depth","title":"MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation","date":"2022-10-14","arxiv_id":"2210.07577","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-to-real-composite-semantic","title":"Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing","date":"2022-10-14","arxiv_id":"2210.07466","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-dense-nuclei-detection-and","title":"Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images","date":"2022-10-14","arxiv_id":"2210.07862","repositories_listed":0,"syntology":null},{"url":null,"slug":"whole-body-tumor-segmentation-of-18f-fdg-pet","title":"Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks","date":"2022-10-14","arxiv_id":"2210.08068","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-adaptive-interactive-thresholding-of","title":"Feature-Adaptive Interactive Thresholding of Large 3D Volumes","date":"2022-10-13","arxiv_id":"2210.06961","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-active-learning-for-segmentation-of","title":"Geometric Active Learning for Segmentation of Large 3D Volumes","date":"2022-10-13","arxiv_id":"2210.06885","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-calibration-of-underrepresented","title":"On the calibration of underrepresented classes in LiDAR-based semantic segmentation","date":"2022-10-13","arxiv_id":"2210.06811","repositories_listed":0,"syntology":null},{"url":null,"slug":"x-align-cross-modal-cross-view-alignment-for","title":"X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation","date":"2022-10-13","arxiv_id":"2210.06778","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-instance-mixing-across-domains","title":"Hierarchical Instance Mixing across Domains in Aerial Segmentation","date":"2022-10-12","arxiv_id":"2210.06216","repositories_listed":0,"syntology":null},{"url":null,"slug":"liveseg-unsupervised-multimodal-temporal","title":"LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos","date":"2022-10-12","arxiv_id":"2210.05840","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-scene-completion-with-joint-color","title":"Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image","date":"2022-10-12","arxiv_id":"2210.05891","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-u-net-uncertainty-in-multi","title":"Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection","date":"2022-10-12","arxiv_id":"2210.06512","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-action-segmentation-from-timestamp","title":"Robust Action Segmentation from Timestamp Supervision","date":"2022-10-12","arxiv_id":"2210.06501","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpanet-dual-pooling-attention-network-for","title":"DPANET:Dual Pooling Attention Network for Semantic Segmentation","date":"2022-10-11","arxiv_id":"2210.05437","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-segmentation-approaches-for-neural","title":"Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text","date":"2022-10-11","arxiv_id":"2210.06990","repositories_listed":0,"syntology":null}],"record_sha256":"f27f0c0ce482878dc8700b566fc80e5ca722a147de853868d561663c832cc16e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}