{"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/semantic-segmentation/papers/121","list_of":"/task/semantic-segmentation","task":"Semantic 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":121,"pages_in_order":148,"rows_per_page":100,"rows":[12001,12100],"of":14763,"counts":{"archive_papers_tagged":14763,"with_a_code_link":6644,"where_syntology_ran_a_sample":1583,"not_listed_spam_title":0,"listed":14763,"listed_where_code_ran":1583,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1384,"every_run_a_failure_of_syntologys_instrument":199,"listed_with_a_run_with_no_instrument_failure":1384,"listed_every_run_a_failure_of_syntologys_instrument":199,"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/semantic-segmentation","prev":"/task/semantic-segmentation/papers/120","next":"/task/semantic-segmentation/papers/122","papers":[{"url":null,"slug":"towards-robust-medical-image-segmentation-on","title":"Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data","date":"2020-11-28","arxiv_id":"2011.14164","repositories_listed":0,"syntology":null},{"url":null,"slug":"descriptor-free-multi-view-region-matching","title":"Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction","date":"2020-11-27","arxiv_id":"2011.13649","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-interpolated-convolutional-network","title":"Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds","date":"2020-11-27","arxiv_id":"2011.13784","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-brain-lesion-volume-in-patches-with","title":"Modelling brain lesion volume in patches with CNN-based Poisson Regression","date":"2020-11-26","arxiv_id":"2011.13927","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-devil-is-in-the-boundary-exploiting","title":"The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation","date":"2020-11-26","arxiv_id":"2011.13241","repositories_listed":0,"syntology":null},{"url":null,"slug":"cellsegmenter-unsupervised-representation","title":"CellSegmenter: unsupervised representation learning and instance segmentation of modular images","date":"2020-11-25","arxiv_id":"2011.12482","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-coupled-with-novel","title":"Deep-learning coupled with novel classification method to classify the urban environment of the developing world","date":"2020-11-25","arxiv_id":"2011.12847","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-feature-driven-active-contour","title":"Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity","date":"2020-11-25","arxiv_id":"2011.12492","repositories_listed":0,"syntology":null},{"url":null,"slug":"pgl-prior-guided-local-self-supervised","title":"PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation","date":"2020-11-25","arxiv_id":"2011.12640","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-registration-for-self-occluded-objects-in","title":"3D Registration for Self-Occluded Objects in Context","date":"2020-11-23","arxiv_id":"2011.11260","repositories_listed":0,"syntology":null},{"url":null,"slug":"argmax-flows-learning-categorical","title":"Argmax Flows: Learning Categorical Distributions with Normalizing Flows","date":"2020-11-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"betrayed-by-motion-camouflaged-object","title":"Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation","date":"2020-11-23","arxiv_id":"2011.11630","repositories_listed":0,"syntology":null},{"url":"/paper/scaling-wide-residual-networks-for-panoptic","slug":"scaling-wide-residual-networks-for-panoptic","title":"Scaling Wide Residual Networks for Panoptic Segmentation","date":"2020-11-23","arxiv_id":"2011.11675","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-model-trained-on-mobile-phone","title":"Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma","date":"2020-11-22","arxiv_id":"2011.11081","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-embedding-network-for-3d-brain","title":"Efficient embedding network for 3D brain tumor segmentation","date":"2020-11-22","arxiv_id":"2011.11052","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-scene-understanding-and-task","title":"Bridging Scene Understanding and Task Execution with Flexible Simulation Environments","date":"2020-11-20","arxiv_id":"2011.10452","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-overlapping-wear-particles-with","title":"Segmentation overlapping wear particles with few labelled data and imbalance sample","date":"2020-11-20","arxiv_id":"2011.10313","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidirectional-rnn-based-few-shot-learning-for","title":"Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation","date":"2020-11-19","arxiv_id":"2011.09608","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-lf-net-semantic-lung-segmentation-from","title":"Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images","date":"2020-11-19","arxiv_id":"2011.09695","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-instance-and-panoptic-segmentation","title":"Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions","date":"2020-11-19","arxiv_id":"2011.09796","repositories_listed":0,"syntology":null},{"url":null,"slug":"softseg-advantages-of-soft-versus-binary","title":"SoftSeg: Advantages of soft versus binary training for image segmentation","date":"2020-11-18","arxiv_id":"2011.09041","repositories_listed":0,"syntology":null},{"url":"/paper/multi-receptive-field-network-for-semantic","slug":"multi-receptive-field-network-for-semantic","title":"Multi Receptive Field Network for Semantic Segmentation","date":"2020-11-17","arxiv_id":"2011.08577","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramid-point-a-multi-level-focusing-network","title":"Pyramid Point: A Multi-Level Focusing Network for Revisiting Feature Layers","date":"2020-11-17","arxiv_id":"2011.08692","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeknet-improved-human-instance-segmentation","title":"SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation","date":"2020-11-17","arxiv_id":"2011.08682","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-level-prior-based-loss-functions-for","title":"High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey","date":"2020-11-16","arxiv_id":"2011.08018","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-medical-image-segmentation-with","title":"Efficient Medical Image Segmentation with Intermediate Supervision Mechanism","date":"2020-11-15","arxiv_id":"2012.03673","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-robustness-of-semantic-segmentation","title":"Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI","date":"2020-11-15","arxiv_id":"2011.07592","repositories_listed":0,"syntology":null},{"url":null,"slug":"w-net-dual-supervised-medical-image","title":"w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution","date":"2020-11-15","arxiv_id":"2012.03674","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-segmentation-in-chest-x-rays-with-res-cr","title":"Lung Segmentation in Chest X-rays with Res-CR-Net","date":"2020-11-14","arxiv_id":"2011.08655","repositories_listed":0,"syntology":null},{"url":null,"slug":"lulc-classification-by-semantic-segmentation","title":"LULC classification by semantic segmentation of satellite images using FastFCN","date":"2020-11-13","arxiv_id":"2011.06825","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-segment-dynamic-objects-using","title":"Learning to Segment Dynamic Objects using SLAM Outliers","date":"2020-11-12","arxiv_id":"2011.06259","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-semi-and-self","title":"A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data","date":"2020-11-11","arxiv_id":"2011.08076","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-and-synergistic-deep-learning","title":"Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images","date":"2020-11-11","arxiv_id":"2011.05791","repositories_listed":0,"syntology":null},{"url":"/paper/learning-from-theodore-a-synthetic","slug":"learning-from-theodore-a-synthetic","title":"Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning","date":"2020-11-11","arxiv_id":"2011.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"scribble-supervised-semantic-segmentation-by","title":"Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace","date":"2020-11-11","arxiv_id":"2011.05621","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-segmentation-via-background","title":"Self-supervised Segmentation via Background Inpainting","date":"2020-11-11","arxiv_id":"2011.05626","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-self-attention-distillation-for","title":"Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation","date":"2020-11-08","arxiv_id":"2011.03908","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-object","title":"Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images","date":"2020-11-06","arxiv_id":"2011.03353","repositories_listed":0,"syntology":null},{"url":null,"slug":"street-to-cloud-improving-flood-maps-with","title":"Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation","date":"2020-11-05","arxiv_id":"2011.08010","repositories_listed":0,"syntology":null},{"url":"/paper/multi-layer-feature-aggregation-for-deep","slug":"multi-layer-feature-aggregation-for-deep","title":"Multi-layer Feature Aggregation for Deep Scene Parsing Models","date":"2020-11-04","arxiv_id":"2011.02572","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-aware-margin-calibration-for","title":"Distribution-aware Margin Calibration for Medical Image Segmentation","date":"2020-11-03","arxiv_id":"2011.01462","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-representations-from-audio-visual","title":"Learning Representations from Audio-Visual Spatial Alignment","date":"2020-11-03","arxiv_id":"2011.01819","repositories_listed":0,"syntology":null},{"url":"/paper/multi-projection-fusion-for-real-time","slug":"multi-projection-fusion-for-real-time","title":"Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds","date":"2020-11-03","arxiv_id":"2011.01974","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-map-based-validation-of-semantic","title":"Towards Map-Based Validation of Semantic Segmentation Masks","date":"2020-11-03","arxiv_id":"2011.08008","repositories_listed":0,"syntology":null},{"url":"/paper/actor-and-action-modular-network-for-text","slug":"actor-and-action-modular-network-for-text","title":"Actor and Action Modular Network for Text-based Video Segmentation","date":"2020-11-02","arxiv_id":"2011.00786","repositories_listed":0,"syntology":null},{"url":null,"slug":"asist-annotation-free-synthetic-instance","title":"ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis","date":"2020-11-02","arxiv_id":"2011.01009","repositories_listed":0,"syntology":null},{"url":null,"slug":"cabinet-efficient-context-aggregation-network","title":"Real-time Semantic Segmentation with Context Aggregation Network","date":"2020-11-02","arxiv_id":"2011.00993","repositories_listed":0,"syntology":null},{"url":null,"slug":"highway-driving-dataset-for-semantic-video","title":"Highway Driving Dataset for Semantic Video Segmentation","date":"2020-11-02","arxiv_id":"2011.00674","repositories_listed":0,"syntology":null},{"url":null,"slug":"marnet-multi-abstraction-refinement-network","title":"MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis","date":"2020-11-02","arxiv_id":"2011.00923","repositories_listed":0,"syntology":null},{"url":null,"slug":"pbp-net-point-projection-and-back-projection","title":"PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation","date":"2020-11-02","arxiv_id":"2011.00988","repositories_listed":0,"syntology":null},{"url":null,"slug":"recyclable-waste-identification-using-cnn","title":"Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering","date":"2020-11-02","arxiv_id":"2011.01353","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-annotation-effort-for-video","title":"Reducing the Annotation Effort for Video Object Segmentation Datasets","date":"2020-11-02","arxiv_id":"2011.01142","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-net-and-its-variants-for-medical-image","title":"U-Net and its variants for medical image segmentation: theory and applications","date":"2020-11-02","arxiv_id":"2011.01118","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolution-neural-networks-for-semantic","title":"Convolution Neural Networks for Semantic Segmentation: Application to Small Datasets of Biomedical Images","date":"2020-11-01","arxiv_id":"2011.01747","repositories_listed":0,"syntology":null},{"url":null,"slug":"un-masked-covid-19-trends-from-social-media","title":"(Un)Masked COVID-19 Trends from Social Media","date":"2020-10-30","arxiv_id":"2011.00052","repositories_listed":0,"syntology":null},{"url":null,"slug":"volumetric-medical-image-segmentation-a-3d","title":"Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples","date":"2020-10-29","arxiv_id":"2010.16074","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-channel-pruning-search","title":"Differentiable Channel Sparsity Search via Weight Sharing within Filters","date":"2020-10-28","arxiv_id":"2010.14714","repositories_listed":0,"syntology":null},{"url":null,"slug":"panoster-end-to-end-panoptic-segmentation-of","title":"Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds","date":"2020-10-28","arxiv_id":"2010.15157","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-awareness-in-intelligent-vehicles","title":"Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection","date":"2020-10-28","arxiv_id":"2010.15056","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasi-spectral-characterization-of","title":"Quasi-spectral characterization of intracellular regions in bright-field light microscopy images","date":"2020-10-27","arxiv_id":"1908.03696","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-centroid-loss-for-weakly-supervised","title":"A Weakly-Supervised Semantic Segmentation Approach based on the Centroid Loss: Application to Quality Control and Inspection","date":"2020-10-26","arxiv_id":"2010.13433","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-image-segmentation-process-using","title":"Global Image Segmentation Process using Machine Learning algorithm & Convolution Neural Network method for Self- Driving Vehicles","date":"2020-10-26","arxiv_id":"2010.13294","repositories_listed":0,"syntology":null},{"url":null,"slug":"lane-detection-in-complex-scenes-based-on-end","title":"Lane detection in complex scenes based on end-to-end neural network","date":"2020-10-26","arxiv_id":"2010.13422","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-for-medical-image-segmentation","title":"Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index","date":"2020-10-26","arxiv_id":"2010.13499","repositories_listed":0,"syntology":null},{"url":null,"slug":"votenet-registration-refinement-for-multi","title":"VoteNet++: Registration Refinement for Multi-Atlas Segmentation","date":"2020-10-26","arxiv_id":"2010.13484","repositories_listed":0,"syntology":null},{"url":null,"slug":"coherent-loss-a-generic-framework-for-stable","title":"Coherent Loss: A Generic Framework for Stable Video Segmentation","date":"2020-10-25","arxiv_id":"2010.13085","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-in-lidar-semantic","title":"Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions","date":"2020-10-23","arxiv_id":"2010.12239","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-the-cortical-plate-in-fetal","title":"Segmentation of the cortical plate in fetal brain MRI with a topological loss","date":"2020-10-23","arxiv_id":"2010.12391","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-scale-permuted-backbone-with-1","title":"Efficient Scale-Permuted Backbone with Learned Resource Distribution","date":"2020-10-22","arxiv_id":"2010.11426","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-sort-image-sequences-via","title":"Learning to Sort Image Sequences via Accumulated Temporal Differences","date":"2020-10-22","arxiv_id":"2010.11649","repositories_listed":0,"syntology":null},{"url":"/paper/posterior-re-calibration-for-imbalanced","slug":"posterior-re-calibration-for-imbalanced","title":"Posterior Re-calibration for Imbalanced Datasets","date":"2020-10-22","arxiv_id":"2010.11820","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/posterior-re-calibration-for-imbalanced#ran","syntology_url":"https://syntology.ai/paper/2010.11820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11820"}},"official":null}},{"url":null,"slug":"task-adaptive-feature-transformer-for-few","title":"Task-Adaptive Feature Transformer for Few-Shot Segmentation","date":"2020-10-22","arxiv_id":"2010.11437","repositories_listed":0,"syntology":null},{"url":null,"slug":"2nd-place-solution-to-instance-segmentation","title":"2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020","date":"2020-10-21","arxiv_id":"2010.10957","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-dual-path-network-for-real-time","title":"Dense Dual-Path Network for Real-time Semantic Segmentation","date":"2020-10-21","arxiv_id":"2010.10778","repositories_listed":0,"syntology":null},{"url":null,"slug":"importance-aware-semantic-segmentation-in","title":"Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training","date":"2020-10-21","arxiv_id":"2010.12440","repositories_listed":0,"syntology":null},{"url":null,"slug":"uav-lidar-point-cloud-segmentation-of-a-stack","title":"UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks","date":"2020-10-21","arxiv_id":"2010.11106","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-wrong-with-continual-learning-in","title":"What is Wrong with Continual Learning in Medical Image Segmentation?","date":"2020-10-21","arxiv_id":"2010.11008","repositories_listed":0,"syntology":null},{"url":null,"slug":"autobss-an-efficient-algorithm-for-block","title":"AutoBSS: An Efficient Algorithm for Block Stacking Style Search","date":"2020-10-20","arxiv_id":"2010.10261","repositories_listed":0,"syntology":null},{"url":null,"slug":"color-image-segmentation-metrics","title":"Color Image Segmentation Metrics","date":"2020-10-19","arxiv_id":"2010.09907","repositories_listed":0,"syntology":null},{"url":null,"slug":"gasnet-weakly-supervised-framework-for-covid","title":"GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation","date":"2020-10-19","arxiv_id":"2010.09456","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-fusion-for-one-click-segmentation","title":"Multi-Stage Fusion for One-Click Segmentation","date":"2020-10-19","arxiv_id":"2010.09672","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-lstm-improving-temporal-awareness-for","title":"Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation","date":"2020-10-19","arxiv_id":"2010.09466","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-learning-for-catheter","title":"Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound","date":"2020-10-19","arxiv_id":"2010.09525","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-interactive-instance-segmentation","title":"Localized Interactive Instance Segmentation","date":"2020-10-18","arxiv_id":"2010.09140","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-constrained-cnn-for-cardiac-mr","title":"Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters","date":"2020-10-18","arxiv_id":"2010.08952","repositories_listed":0,"syntology":null},{"url":null,"slug":"lid-2020-the-learning-from-imperfect-data","title":"LID 2020: The Learning from Imperfect Data Challenge Results","date":"2020-10-17","arxiv_id":"2010.11724","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-iterative-training-of-convolutional","title":"Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology","date":"2020-10-16","arxiv_id":"2010.08296","repositories_listed":0,"syntology":null},{"url":null,"slug":"ct-image-segmentation-for-inflamed-and","title":"CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network","date":"2020-10-16","arxiv_id":"2010.08582","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensembling-low-precision-models-for-binary","title":"Ensembling Low Precision Models for Binary Biomedical Image Segmentation","date":"2020-10-16","arxiv_id":"2010.08648","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-perception-based-evaluation-criterion-1","title":"Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation","date":"2020-10-16","arxiv_id":"2010.08209","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-accurate-person-level-action","title":"Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes","date":"2020-10-16","arxiv_id":"2010.08365","repositories_listed":0,"syntology":null},{"url":null,"slug":"zoom-cam-generating-fine-grained-pixel","title":"Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels","date":"2020-10-16","arxiv_id":"2010.08644","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-closed-loop-system-for-improving-annotation","title":"A Closed-Loop System for Improving Annotation Quality and Efficiency","date":"2020-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynaslam-ii-tightly-coupled-multi-object","title":"DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM","date":"2020-10-15","arxiv_id":"2010.07820","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoder-decoder-semantic-segmentation-models","title":"Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules","date":"2020-10-15","arxiv_id":"2010.07556","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-semantic-segmentation-in","title":"Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study","date":"2020-10-15","arxiv_id":"2010.07830","repositories_listed":0,"syntology":null},{"url":null,"slug":"pp-linknet-improving-semantic-segmentation-of","title":"PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training","date":"2020-10-14","arxiv_id":"2010.06932","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-flow-guided-motion-removal-method","title":"Semantic Flow-guided Motion Removal Method for Robust Mapping","date":"2020-10-14","arxiv_id":"2010.06876","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-for-partially-occluded","title":"Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning","date":"2020-10-14","arxiv_id":"2010.06879","repositories_listed":0,"syntology":null},{"url":null,"slug":"weightalign-normalizing-activations-by-weight","title":"WeightAlign: Normalizing Activations by Weight Alignment","date":"2020-10-14","arxiv_id":"2010.07160","repositories_listed":0,"syntology":null}],"record_sha256":"1504508b0ba063ca10d0b7047c5ceea07beda6f4bce710be243f5a03723c5929","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}