{"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/49","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":49,"pages_in_order":131,"rows_per_page":100,"rows":[4801,4900],"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/48","next":"/task/segmentation/papers/50","papers":[{"url":"/paper/boundary-and-entropy-driven-adversarial","slug":"boundary-and-entropy-driven-adversarial","title":"Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation","date":"2019-06-26","arxiv_id":"1906.11143","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-dice-coefficient-a-method-for","slug":"continuous-dice-coefficient-a-method-for","title":"Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations","date":"2019-06-26","arxiv_id":"1906.11031","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/continuous-dice-coefficient-a-method-for#ran","syntology_url":"https://syntology.ai/paper/1906.11031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.11031"}},"official":null}},{"url":"/paper/refined-segmentation-r-cnn-a-two-stage","slug":"refined-segmentation-r-cnn-a-two-stage","title":"Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants","date":"2019-06-24","arxiv_id":"1906.09684","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automatic-liver-attenuation-estimation","slug":"fully-automatic-liver-attenuation-estimation","title":"Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations","date":"2019-06-23","arxiv_id":"1906.09549","repositories_listed":1,"syntology":null},{"url":"/paper/the-second-dihard-diarization-challenge","slug":"the-second-dihard-diarization-challenge","title":"The Second DIHARD Diarization Challenge: Dataset, task, and baselines","date":"2019-06-18","arxiv_id":"1906.07839","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-temporal-segmentation-by-nonlocal","slug":"enhancing-temporal-segmentation-by-nonlocal","title":"Enhancing temporal segmentation by nonlocal self-similarity","date":"2019-06-14","arxiv_id":"1906.11335","repositories_listed":1,"syntology":null},{"url":"/paper/learning-instance-occlusion-for-panoptic","slug":"learning-instance-occlusion-for-panoptic","title":"Learning Instance Occlusion for Panoptic Segmentation","date":"2019-06-13","arxiv_id":"1906.05896","repositories_listed":1,"syntology":null},{"url":"/paper/show-match-and-segment-joint-learning-of","slug":"show-match-and-segment-joint-learning-of","title":"Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation","date":"2019-06-13","arxiv_id":"1906.05857","repositories_listed":1,"syntology":null},{"url":"/paper/labeling-cutting-grouping-an-efficient-text","slug":"labeling-cutting-grouping-an-efficient-text","title":"Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts","date":"2019-06-11","arxiv_id":"1906.11894","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-guided-attention-for-medical","slug":"multi-scale-guided-attention-for-medical","title":"Multi-scale self-guided attention for medical image segmentation","date":"2019-06-07","arxiv_id":"1906.02849","repositories_listed":1,"syntology":null},{"url":"/paper/when-unseen-domain-generalization-is","slug":"when-unseen-domain-generalization-is","title":"When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation","date":"2019-06-07","arxiv_id":"1906.03347","repositories_listed":1,"syntology":null},{"url":"/paper/handling-inter-annotator-agreement-for","slug":"handling-inter-annotator-agreement-for","title":"Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation","date":"2019-06-06","arxiv_id":"1906.02415","repositories_listed":1,"syntology":null},{"url":"/paper/190602343","slug":"190602343","title":"Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders","date":"2019-06-05","arxiv_id":"1906.02343","repositories_listed":1,"syntology":null},{"url":"/paper/an-uncertainty-driven-gcn-refinement-strategy","slug":"an-uncertainty-driven-gcn-refinement-strategy","title":"Uncertainty-based graph convolutional networks for organ segmentation refinement","date":"2019-06-05","arxiv_id":"1906.02191","repositories_listed":1,"syntology":null},{"url":"/paper/learning-shape-representation-on-sparse-point","slug":"learning-shape-representation-on-sparse-point","title":"Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation","date":"2019-06-05","arxiv_id":"1906.02281","repositories_listed":1,"syntology":null},{"url":"/paper/one-pass-multi-task-networks-with-cross-task","slug":"one-pass-multi-task-networks-with-cross-task","title":"One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation","date":"2019-06-05","arxiv_id":"1906.01796","repositories_listed":1,"syntology":null},{"url":"/paper/improving-neural-language-models-by","slug":"improving-neural-language-models-by","title":"Improving Neural Language Models by Segmenting, Attending, and Predicting the Future","date":"2019-06-04","arxiv_id":"1906.01702","repositories_listed":1,"syntology":null},{"url":"/paper/190600790","slug":"190600790","title":"Multi-task Pairwise Neural Ranking for Hashtag Segmentation","date":"2019-06-03","arxiv_id":"1906.00790","repositories_listed":1,"syntology":null},{"url":"/paper/low-rank-random-tensor-for-bilinear-pooling","slug":"low-rank-random-tensor-for-bilinear-pooling","title":"Frontal Low-rank Random Tensors for Fine-grained Action Segmentation","date":"2019-06-03","arxiv_id":"1906.01004","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-pyramid-context-network-for-semantic","slug":"adaptive-pyramid-context-network-for-semantic","title":"Adaptive Pyramid Context Network for Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/amodal-instance-segmentation-with-kins","slug":"amodal-instance-segmentation-with-kins","title":"Amodal Instance Segmentation With KINS Dataset","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-encoding-strategy-based-word-character","slug":"an-encoding-strategy-based-word-character","title":"An Encoding Strategy Based Word-Character LSTM for Chinese NER","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/clustering-based-article-identification-in","slug":"clustering-based-article-identification-in","title":"Clustering-Based Article Identification in Historical Newspapers","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cyclic-guidance-for-weakly-supervised-joint","slug":"cyclic-guidance-for-weakly-supervised-joint","title":"Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deepco3-deep-instance-co-segmentation-by-co","slug":"deepco3-deep-instance-co-segmentation-by-co","title":"DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/geometry-aware-distillation-for-indoor","slug":"geometry-aware-distillation-for-indoor","title":"Geometry-Aware Distillation for Indoor Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/graph-attention-convolution-for-point-cloud","slug":"graph-attention-convolution-for-point-cloud","title":"Graph Attention Convolution for Point Cloud Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-word-attention-into-character","slug":"incorporating-word-attention-into-character","title":"Incorporating Word Attention into Character-Based Word Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-unsupervised-video-object","slug":"learning-unsupervised-video-object","title":"Learning Unsupervised Video Object Segmentation Through Visual Attention","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/object-instance-annotation-with-deep-extreme","slug":"object-instance-annotation-with-deep-extreme","title":"Object Instance Annotation With Deep Extreme Level Set Evolution","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pointweb-enhancing-local-neighborhood","slug":"pointweb-enhancing-local-neighborhood","title":"PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-projection-network-for-zero-and-few","slug":"semantic-projection-network-for-zero-and-few","title":"Semantic Projection Network for Zero- and Few-Label Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/structured-knowledge-distillation-for-1","slug":"structured-knowledge-distillation-for-1","title":"Structured Knowledge Distillation for Semantic Segmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-semantics-aware-distance-map-with","slug":"learning-semantics-aware-distance-map-with","title":"Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation","date":"2019-05-30","arxiv_id":"1905.12898","repositories_listed":1,"syntology":null},{"url":"/paper/training-generative-adversarial-networks-from","slug":"training-generative-adversarial-networks-from","title":"Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators","date":"2019-05-29","arxiv_id":"1905.12660","repositories_listed":1,"syntology":null},{"url":"/paper/190513539","slug":"190513539","title":"Unsupervised Object Segmentation by Redrawing","date":"2019-05-27","arxiv_id":"1905.13539","repositories_listed":1,"syntology":null},{"url":"/paper/straight-to-shapes-real-time-instance","slug":"straight-to-shapes-real-time-instance","title":"Straight to Shapes++: Real-time Instance Segmentation Made More Accurate","date":"2019-05-27","arxiv_id":"1905.11358","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-temporal-information-for-improved","slug":"exploring-temporal-information-for-improved","title":"Exploring Temporal Information for Improved Video Understanding","date":"2019-05-25","arxiv_id":"1905.10654","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-domain-knowledge-to-improve-em","slug":"leveraging-domain-knowledge-to-improve-em","title":"Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts","date":"2019-05-25","arxiv_id":"1905.10535","repositories_listed":1,"syntology":null},{"url":"/paper/acnet-attention-based-network-to-exploit","slug":"acnet-attention-based-network-to-exploit","title":"ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation","date":"2019-05-24","arxiv_id":"1905.10089","repositories_listed":1,"syntology":null},{"url":"/paper/190513306","slug":"190513306","title":"Implicit Background Estimation for Semantic Segmentation","date":"2019-05-23","arxiv_id":"1905.13306","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-learned-random-walker-for-seeded-1","slug":"end-to-end-learned-random-walker-for-seeded-1","title":"End-to-End Learned Random Walker for Seeded Image Segmentation","date":"2019-05-22","arxiv_id":"1905.09045","repositories_listed":1,"syntology":null},{"url":"/paper/robust-motion-segmentation-from-pairwise","slug":"robust-motion-segmentation-from-pairwise","title":"Robust Motion Segmentation from Pairwise Matches","date":"2019-05-22","arxiv_id":"1905.09043","repositories_listed":1,"syntology":null},{"url":"/paper/rasnet-segmentation-for-tracking-surgical","slug":"rasnet-segmentation-for-tracking-surgical","title":"RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network","date":"2019-05-21","arxiv_id":"1905.08663","repositories_listed":1,"syntology":null},{"url":"/paper/hmms-for-unsupervised-vietnamese","slug":"hmms-for-unsupervised-vietnamese","title":"HMMs for Unsupervised Vietnamese WordSegmentation","date":"2019-05-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/190506368","slug":"190506368","title":"Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images","date":"2019-05-15","arxiv_id":"1905.06368","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/190506368#ran","syntology_url":"https://syntology.ai/paper/1905.06368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06368"}},"official":{"repos":["chenwydj/ultra_high_resolution_segmentation"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/190508611","slug":"190508611","title":"Machine learning approach for segmenting glands in colon histology images using local intensity and texture features","date":"2019-05-15","arxiv_id":"1905.08611","repositories_listed":1,"syntology":null},{"url":"/paper/edgesegnet-a-compact-network-for-semantic","slug":"edgesegnet-a-compact-network-for-semantic","title":"EdgeSegNet: A Compact Network for Semantic Segmentation","date":"2019-05-10","arxiv_id":"1905.04222","repositories_listed":1,"syntology":null},{"url":"/paper/supervized-segmentation-with-graph-structured","slug":"supervized-segmentation-with-graph-structured","title":"Supervized Segmentation with Graph-Structured Deep Metric Learning","date":"2019-05-10","arxiv_id":"1905.04014","repositories_listed":1,"syntology":null},{"url":"/paper/190503639","slug":"190503639","title":"Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function","date":"2019-05-09","arxiv_id":"1905.03639","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-domain-adaptation-using-3","slug":"unsupervised-domain-adaptation-using-3","title":"Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images","date":"2019-05-08","arxiv_id":"1905.03198","repositories_listed":1,"syntology":null},{"url":"/paper/image-recoloring-based-on-object-color","slug":"image-recoloring-based-on-object-color","title":"Image Recoloring Based on Object Color Distributions","date":"2019-05-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/simultaneous-object-detection-and-semantic","slug":"simultaneous-object-detection-and-semantic","title":"Simultaneous Object Detection and Semantic Segmentation","date":"2019-05-06","arxiv_id":"1905.02285","repositories_listed":1,"syntology":null},{"url":"/paper/scops-self-supervised-co-part-segmentation","slug":"scops-self-supervised-co-part-segmentation","title":"SCOPS: Self-Supervised Co-Part Segmentation","date":"2019-05-03","arxiv_id":"1905.01298","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-of-video-sequences-with","slug":"semantic-segmentation-of-video-sequences-with","title":"Semantic Segmentation of Video Sequences with Convolutional LSTMs","date":"2019-05-03","arxiv_id":"1905.01058","repositories_listed":1,"syntology":null},{"url":"/paper/190411126","slug":"190411126","title":"Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks","date":"2019-04-25","arxiv_id":"1904.11126","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-deep-learning-for-bayesian-brain","slug":"unsupervised-deep-learning-for-bayesian-brain","title":"Unsupervised Deep Learning for Bayesian Brain MRI Segmentation","date":"2019-04-25","arxiv_id":"1904.11319","repositories_listed":1,"syntology":null},{"url":"/paper/gumdrop-at-the-disrpt2019-shared-task-a-model","slug":"gumdrop-at-the-disrpt2019-shared-task-a-model","title":"GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection","date":"2019-04-23","arxiv_id":"1904.10419","repositories_listed":1,"syntology":null},{"url":"/paper/fast-user-guided-video-object-segmentation-by","slug":"fast-user-guided-video-object-segmentation-by","title":"Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks","date":"2019-04-22","arxiv_id":"1904.09791","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-the-hausdorff-distance-in-medical","slug":"reducing-the-hausdorff-distance-in-medical","title":"Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks","date":"2019-04-22","arxiv_id":"1904.10030","repositories_listed":1,"syntology":null},{"url":"/paper/deepatlas-joint-semi-supervised-learning-of","slug":"deepatlas-joint-semi-supervised-learning-of","title":"DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation","date":"2019-04-17","arxiv_id":"1904.08465","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deepatlas-joint-semi-supervised-learning-of#ran","syntology_url":"https://syntology.ai/paper/1904.08465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08465"}},"official":null}},{"url":"/paper/190408017","slug":"190408017","title":"A-CNN: Annularly Convolutional Neural Networks on Point Clouds","date":"2019-04-16","arxiv_id":"1904.08017","repositories_listed":1,"syntology":null},{"url":"/paper/a-systematic-study-of-leveraging-subword","slug":"a-systematic-study-of-leveraging-subword","title":"A Systematic Study of Leveraging Subword Information for Learning Word Representations","date":"2019-04-16","arxiv_id":"1904.07994","repositories_listed":1,"syntology":null},{"url":"/paper/cloudsegnet-a-deep-network-for-nychthemeron","slug":"cloudsegnet-a-deep-network-for-nychthemeron","title":"CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation","date":"2019-04-16","arxiv_id":"1904.07979","repositories_listed":1,"syntology":null},{"url":"/paper/uni-em-an-environment-for-deep-neural-network","slug":"uni-em-an-environment-for-deep-neural-network","title":"UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images","date":"2019-04-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/actor-critic-instance-segmentation","slug":"actor-critic-instance-segmentation","title":"Actor-Critic Instance Segmentation","date":"2019-04-10","arxiv_id":"1904.05126","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-semi-supervised-segmentation","slug":"curriculum-semi-supervised-segmentation","title":"Curriculum semi-supervised segmentation","date":"2019-04-10","arxiv_id":"1904.05236","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-of-biological-images","slug":"instance-segmentation-of-biological-images","title":"Instance Segmentation of Biological Images Using Harmonic Embeddings","date":"2019-04-10","arxiv_id":"1904.05257","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-model-for-joint-chinese-word","slug":"a-unified-model-for-joint-chinese-word","title":"A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing","date":"2019-04-09","arxiv_id":"1904.04697","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-measures-and-prediction-quality","slug":"uncertainty-measures-and-prediction-quality","title":"Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images","date":"2019-04-09","arxiv_id":"1904.04516","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-morphological-reconstruction-for","slug":"adaptive-morphological-reconstruction-for","title":"Adaptive Morphological Reconstruction for Seeded Image Segmentation","date":"2019-04-08","arxiv_id":"1904.03973","repositories_listed":1,"syntology":null},{"url":"/paper/3d-dilated-multi-fiber-network-for-real-time","slug":"3d-dilated-multi-fiber-network-for-real-time","title":"3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI","date":"2019-04-06","arxiv_id":"1904.03355","repositories_listed":1,"syntology":null},{"url":"/paper/the-fishyscapes-benchmark-measuring-blind","slug":"the-fishyscapes-benchmark-measuring-blind","title":"The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation","date":"2019-04-05","arxiv_id":"1904.03215","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-action-segmentation-using","slug":"weakly-supervised-action-segmentation-using","title":"Fast Weakly Supervised Action Segmentation Using Mutual Consistency","date":"2019-04-05","arxiv_id":"1904.03116","repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-discrete-convolutions-for","slug":"generalizing-discrete-convolutions-for","title":"ConvPoint: Continuous Convolutions for Point Cloud Processing","date":"2019-04-04","arxiv_id":"1904.02375","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generalizing-discrete-convolutions-for#ran","syntology_url":"https://syntology.ai/paper/1904.02375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02375"}},"official":{"repos":["aboulch/ConvPoint"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/spatiotemporal-cnn-for-video-object","slug":"spatiotemporal-cnn-for-video-object","title":"Spatiotemporal CNN for Video Object Segmentation","date":"2019-04-04","arxiv_id":"1904.02363","repositories_listed":1,"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":1,"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/spatiotemporal-cnn-for-video-object#ran","syntology_url":"https://syntology.ai/paper/1904.02363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02363"}},"official":{"repos":["longyin880815/STCNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/can-ner-convolutional-attention-network","slug":"can-ner-convolutional-attention-network","title":"CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition","date":"2019-04-03","arxiv_id":"1904.02141","repositories_listed":1,"syntology":null},{"url":"/paper/mavnet-an-effective-semantic-segmentation","slug":"mavnet-an-effective-semantic-segmentation","title":"MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks","date":"2019-04-03","arxiv_id":"1904.01795","repositories_listed":1,"syntology":null},{"url":"/paper/event-based-motion-segmentation-by-motion","slug":"event-based-motion-segmentation-by-motion","title":"Event-Based Motion Segmentation by Motion Compensation","date":"2019-04-02","arxiv_id":"1904.01293","repositories_listed":1,"syntology":null},{"url":"/paper/jsis3d-joint-semantic-instance-segmentation","slug":"jsis3d-joint-semantic-instance-segmentation","title":"JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields","date":"2019-04-01","arxiv_id":"1904.00699","repositories_listed":1,"syntology":null},{"url":"/paper/standardized-assessment-of-automatic","slug":"standardized-assessment-of-automatic","title":"Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge","date":"2019-04-01","arxiv_id":"1904.00682","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/standardized-assessment-of-automatic#ran","syntology_url":"https://syntology.ai/paper/1904.00682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00682"}},"official":{"repos":["hjkuijf/wmhchallenge"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mortonnet-self-supervised-learning-of-local","slug":"mortonnet-self-supervised-learning-of-local","title":"MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds","date":"2019-03-30","arxiv_id":"1904.00230","repositories_listed":1,"syntology":null},{"url":"/paper/bubblenets-learning-to-select-the-guidance","slug":"bubblenets-learning-to-select-the-guidance","title":"BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames","date":"2019-03-28","arxiv_id":"1903.11779","repositories_listed":1,"syntology":null},{"url":"/paper/all-about-structure-adapting-structural","slug":"all-about-structure-adapting-structural","title":"All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation","date":"2019-03-26","arxiv_id":"1903.12212","repositories_listed":1,"syntology":null},{"url":"/paper/what-does-ai-see-deep-segmentation-networks","slug":"what-does-ai-see-deep-segmentation-networks","title":"Deep segmentation networks predict survival of non-small cell lung cancer","date":"2019-03-26","arxiv_id":"1903.11593","repositories_listed":1,"syntology":null},{"url":"/paper/residual-pyramid-learning-for-single-shot","slug":"residual-pyramid-learning-for-single-shot","title":"Residual Pyramid Learning for Single-Shot Semantic Segmentation","date":"2019-03-23","arxiv_id":"1903.09746","repositories_listed":1,"syntology":null},{"url":"/paper/overcoming-small-minirhizotron-datasets-using","slug":"overcoming-small-minirhizotron-datasets-using","title":"Overcoming Small Minirhizotron Datasets Using Transfer Learning","date":"2019-03-22","arxiv_id":"1903.09344","repositories_listed":1,"syntology":null},{"url":"/paper/dilated-deeply-supervised-networks-for","slug":"dilated-deeply-supervised-networks-for","title":"Dilated deeply supervised networks for hippocampus segmentation in MRI","date":"2019-03-20","arxiv_id":"1903.09097","repositories_listed":1,"syntology":null},{"url":"/paper/a-smartphone-application-to-detection-and","slug":"a-smartphone-application-to-detection-and","title":"A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust","date":"2019-03-19","arxiv_id":"1904.00742","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-smoothing-of-dilated-convolutions","slug":"efficient-smoothing-of-dilated-convolutions","title":"Efficient Smoothing of Dilated Convolutions for Image Segmentation","date":"2019-03-19","arxiv_id":"1903.07992","repositories_listed":1,"syntology":null},{"url":"/paper/preconditioned-p-ula-for-joint-deconvolution","slug":"preconditioned-p-ula-for-joint-deconvolution","title":"Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images -- Extended Version","date":"2019-03-19","arxiv_id":"1903.08111","repositories_listed":1,"syntology":null},{"url":"/paper/fast-determination-of-coarse-grained-cell","slug":"fast-determination-of-coarse-grained-cell","title":"Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform","date":"2019-03-17","arxiv_id":"1810.11652","repositories_listed":1,"syntology":null},{"url":"/paper/a-cross-season-correspondence-dataset-for","slug":"a-cross-season-correspondence-dataset-for","title":"A Cross-Season Correspondence Dataset for Robust Semantic Segmentation","date":"2019-03-16","arxiv_id":"1903.06916","repositories_listed":1,"syntology":null},{"url":"/paper/blvd-building-a-large-scale-5d-semantics","slug":"blvd-building-a-large-scale-5d-semantics","title":"BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving","date":"2019-03-15","arxiv_id":"1903.06405","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/blvd-building-a-large-scale-5d-semantics#ran","syntology_url":"https://syntology.ai/paper/1903.06405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.06405"}},"official":{"repos":["VCCIV/BLVD"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-label-cloud-segmentation-using-a-deep","slug":"multi-label-cloud-segmentation-using-a-deep","title":"Multi-label Cloud Segmentation Using a Deep Network","date":"2019-03-15","arxiv_id":"1903.06562","repositories_listed":1,"syntology":null},{"url":"/paper/rtfnet-rgb-thermal-fusion-network-for","slug":"rtfnet-rgb-thermal-fusion-network-for","title":"RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes","date":"2019-03-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rvos-end-to-end-recurrent-network-for-video","slug":"rvos-end-to-end-recurrent-network-for-video","title":"RVOS: End-to-End Recurrent Network for Video Object Segmentation","date":"2019-03-13","arxiv_id":"1903.05612","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-adaptation-for-efficient-semantic","slug":"knowledge-adaptation-for-efficient-semantic","title":"Knowledge Adaptation for Efficient Semantic Segmentation","date":"2019-03-12","arxiv_id":"1903.04688","repositories_listed":1,"syntology":null},{"url":"/paper/shape2motion-joint-analysis-of-motion-parts","slug":"shape2motion-joint-analysis-of-motion-parts","title":"Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes","date":"2019-03-10","arxiv_id":"1903.03911","repositories_listed":1,"syntology":null},{"url":"/paper/on-boosting-semantic-street-scene","slug":"on-boosting-semantic-street-scene","title":"On Boosting Semantic Street Scene Segmentation with Weak Supervision","date":"2019-03-08","arxiv_id":"1903.03462","repositories_listed":1,"syntology":null}],"record_sha256":"120bc16240117a846fbac6f0909eacad201f93aad2b28d8ab7877c0382f763ff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}