{"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/48","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":48,"pages_in_order":131,"rows_per_page":100,"rows":[4701,4800],"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/47","next":"/task/segmentation/papers/49","papers":[{"url":"/paper/co-segmentation-inspired-attention-networks","slug":"co-segmentation-inspired-attention-networks","title":"Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-segmentation-networks-self-1","slug":"fine-grained-segmentation-networks-self-1","title":"Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/group-wise-deep-object-co-segmentation-with","slug":"group-wise-deep-object-co-segmentation-with","title":"Group-Wise Deep Object Co-Segmentation With Co-Attention Recurrent Neural Network","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/histosegnet-semantic-segmentation-of","slug":"histosegnet-semantic-segmentation-of","title":"HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vtnfp-an-image-based-virtual-try-on-network","slug":"vtnfp-an-image-based-virtual-try-on-network","title":"VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/capsulevos-semi-supervised-video-object","slug":"capsulevos-semi-supervised-video-object","title":"CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing","date":"2019-09-30","arxiv_id":"1910.00132","repositories_listed":1,"syntology":null},{"url":"/paper/track-to-reconstruct-and-reconstruct-to-track","slug":"track-to-reconstruct-and-reconstruct-to-track","title":"Track to Reconstruct and Reconstruct to Track","date":"2019-09-30","arxiv_id":"1910.00130","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/track-to-reconstruct-and-reconstruct-to-track#ran","syntology_url":"https://syntology.ai/paper/1910.00130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00130"}},"official":{"repos":["tobiasfshr/MOTSFusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/feature-weighting-and-boosting-for-few-shot","slug":"feature-weighting-and-boosting-for-few-shot","title":"Feature Weighting and Boosting for Few-Shot Segmentation","date":"2019-09-28","arxiv_id":"1909.13140","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-energy-based-learning-for","slug":"weakly-supervised-energy-based-learning-for","title":"Weakly Supervised Energy-Based Learning for Action Segmentation","date":"2019-09-28","arxiv_id":"1909.13155","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-roi-generation-for-video-object","slug":"adaptive-roi-generation-for-video-object","title":"Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning","date":"2019-09-27","arxiv_id":"1909.12482","repositories_listed":1,"syntology":null},{"url":"/paper/invisible-marker-automatic-annotation-for","slug":"invisible-marker-automatic-annotation-for","title":"Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation","date":"2019-09-27","arxiv_id":"1909.12493","repositories_listed":1,"syntology":null},{"url":"/paper/190910360","slug":"190910360","title":"RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments","date":"2019-09-23","arxiv_id":"1909.10360","repositories_listed":1,"syntology":null},{"url":"/paper/190909716","slug":"190909716","title":"Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data","date":"2019-09-20","arxiv_id":"1909.09716","repositories_listed":1,"syntology":null},{"url":"/paper/acfnet-attentional-class-feature-network-for","slug":"acfnet-attentional-class-feature-network-for","title":"ACFNet: Attentional Class Feature Network for Semantic Segmentation","date":"2019-09-20","arxiv_id":"1909.09408","repositories_listed":1,"syntology":null},{"url":"/paper/pst900-rgb-thermal-calibration-dataset-and","slug":"pst900-rgb-thermal-calibration-dataset-and","title":"PST900: RGB-Thermal Calibration, Dataset and Segmentation Network","date":"2019-09-20","arxiv_id":"1909.10980","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-atlases-to-enforce-topological","slug":"probabilistic-atlases-to-enforce-topological","title":"Probabilistic Atlases to Enforce Topological Constraints","date":"2019-09-18","arxiv_id":"1909.08330","repositories_listed":1,"syntology":null},{"url":"/paper/ds-pass-detail-sensitive-panoramic-annular","slug":"ds-pass-detail-sensitive-panoramic-annular","title":"DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing","date":"2019-09-17","arxiv_id":"1909.07721","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-3d-fully-convolutional-networks-for","slug":"efficient-3d-fully-convolutional-networks-for","title":"Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images","date":"2019-09-16","arxiv_id":"1909.07474","repositories_listed":1,"syntology":null},{"url":"/paper/an-automatic-cardiac-segmentation-framework","slug":"an-automatic-cardiac-segmentation-framework","title":"An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image","date":"2019-09-12","arxiv_id":"1909.05488","repositories_listed":1,"syntology":null},{"url":"/paper/cerebrum-a-convolutional-encoder-decoder-for","slug":"cerebrum-a-convolutional-encoder-decoder-for","title":"CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI","date":"2019-09-11","arxiv_id":"1909.05085","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-policy-gradient-for-deep-learning","slug":"adversarial-policy-gradient-for-deep-learning","title":"Adversarial Policy Gradient for Deep Learning Image Augmentation","date":"2019-09-09","arxiv_id":"1909.04108","repositories_listed":1,"syntology":null},{"url":"/paper/joint-learning-of-saliency-detection-and","slug":"joint-learning-of-saliency-detection-and","title":"Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation","date":"2019-09-09","arxiv_id":"1909.04161","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-scale-equivariant-network-for","slug":"self-supervised-scale-equivariant-network-for","title":"Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation","date":"2019-09-09","arxiv_id":"1909.03714","repositories_listed":1,"syntology":null},{"url":"/paper/a-resource-efficient-embedded-iris","slug":"a-resource-efficient-embedded-iris","title":"A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks","date":"2019-09-08","arxiv_id":"1909.03385","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-correlation-promoted-shape-variant-1","slug":"semantic-correlation-promoted-shape-variant-1","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","date":"2019-09-05","arxiv_id":"1909.02651","repositories_listed":1,"syntology":null},{"url":"/paper/3d-u2-net-a-3d-universal-u-net-for-multi","slug":"3d-u2-net-a-3d-universal-u-net-for-multi","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","date":"2019-09-04","arxiv_id":"1909.06012","repositories_listed":1,"syntology":null},{"url":"/paper/demystifying-brain-tumour-segmentation","slug":"demystifying-brain-tumour-segmentation","title":"Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis","date":"2019-09-03","arxiv_id":"1909.01498","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-of-panoramic-images","slug":"semantic-segmentation-of-panoramic-images","title":"Semantic Segmentation of Panoramic Images Using a Synthetic Dataset","date":"2019-09-02","arxiv_id":"1909.00532","repositories_listed":1,"syntology":null},{"url":"/paper/a-semi-automated-usability-evaluation","slug":"a-semi-automated-usability-evaluation","title":"A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems","date":"2019-09-01","arxiv_id":"1909.00482","repositories_listed":1,"syntology":null},{"url":"/paper/multi-sensor-cloud-and-cloud-shadow","slug":"multi-sensor-cloud-and-cloud-shadow","title":"Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network","date":"2019-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/toward-better-boundary-preserved-supervoxel","slug":"toward-better-boundary-preserved-supervoxel","title":"Toward better boundary preserved supervoxel segmentation for 3D point clouds","date":"2019-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/boundary-aware-feature-propagation-for-scene","slug":"boundary-aware-feature-propagation-for-scene","title":"Boundary-Aware Feature Propagation for Scene Segmentation","date":"2019-08-31","arxiv_id":"1909.00179","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/boundary-aware-feature-propagation-for-scene#ran","syntology_url":"https://syntology.ai/paper/1909.00179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00179"}},"official":{"repos":["henghuiding/BFP"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/detecting-floodwater-on-roadways-from-image","slug":"detecting-floodwater-on-roadways-from-image","title":"Detecting floodwater on roadways from image data with handcrafted features and deep transfer learning","date":"2019-08-31","arxiv_id":"1909.00125","repositories_listed":1,"syntology":null},{"url":"/paper/class-based-styling-real-time-localized-style","slug":"class-based-styling-real-time-localized-style","title":"Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation","date":"2019-08-30","arxiv_id":"1908.11525","repositories_listed":1,"syntology":null},{"url":"/paper/lu-net-an-efficient-network-for-3d-lidar","slug":"lu-net-an-efficient-network-for-3d-lidar","title":"LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net","date":"2019-08-30","arxiv_id":"1908.11656","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-cyclegan-for-semi-supervised","slug":"revisiting-cyclegan-for-semi-supervised","title":"Revisiting CycleGAN for semi-supervised segmentation","date":"2019-08-30","arxiv_id":"1908.11569","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-temporality-for-semi-supervised","slug":"exploiting-temporality-for-semi-supervised","title":"Exploiting Temporality for Semi-Supervised Video Segmentation","date":"2019-08-29","arxiv_id":"1908.11309","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-blur-detection-from","slug":"self-supervised-blur-detection-from","title":"Self-supervised blur detection from synthetically blurred scenes","date":"2019-08-28","arxiv_id":"1908.10638","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-mask-guided-end-to-end-person","slug":"segmentation-mask-guided-end-to-end-person","title":"Segmentation Mask Guided End-to-End Person Search","date":"2019-08-27","arxiv_id":"1908.10179","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-self-motivated-pyramid","slug":"constructing-self-motivated-pyramid","title":"Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach","date":"2019-08-26","arxiv_id":"1908.09547","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-convolutional-networks-with-weak","slug":"adversarial-convolutional-networks-with-weak","title":"Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation","date":"2019-08-25","arxiv_id":"1908.09298","repositories_listed":1,"syntology":null},{"url":"/paper/where-is-my-mirror","slug":"where-is-my-mirror","title":"Where Is My Mirror?","date":"2019-08-24","arxiv_id":"1908.09101","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-input-configuration-of-dynamic","slug":"optimal-input-configuration-of-dynamic","title":"Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation","date":"2019-08-22","arxiv_id":"1908.08251","repositories_listed":1,"syntology":null},{"url":"/paper/disco-for-the-cia-deep-learning-instance","slug":"disco-for-the-cia-deep-learning-instance","title":"DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging","date":"2019-08-21","arxiv_id":"1908.07957","repositories_listed":1,"syntology":null},{"url":"/paper/deep-active-lesion-segmentation","slug":"deep-active-lesion-segmentation","title":"Deep Active Lesion Segmentation","date":"2019-08-19","arxiv_id":"1908.06933","repositories_listed":1,"syntology":null},{"url":"/paper/irnet-instance-relation-network-for","slug":"irnet-instance-relation-network-for","title":"IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation","date":"2019-08-19","arxiv_id":"1908.06623","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-segmentation-networks-self","slug":"fine-grained-segmentation-networks-self","title":"Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization","date":"2019-08-18","arxiv_id":"1908.06387","repositories_listed":1,"syntology":null},{"url":"/paper/shellnet-efficient-point-cloud-convolutional","slug":"shellnet-efficient-point-cloud-convolutional","title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","date":"2019-08-17","arxiv_id":"1908.06295","repositories_listed":1,"syntology":null},{"url":"/paper/multi-step-cascaded-networks-for-brain-tumor","slug":"multi-step-cascaded-networks-for-brain-tumor","title":"Multi-step Cascaded Networks for Brain Tumor Segmentation","date":"2019-08-16","arxiv_id":"1908.05887","repositories_listed":1,"syntology":null},{"url":"/paper/a-single-shot-arbitrarily-shaped-text","slug":"a-single-shot-arbitrarily-shaped-text","title":"A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning","date":"2019-08-15","arxiv_id":"1908.05498","repositories_listed":1,"syntology":null},{"url":"/paper/ps2-net-a-locally-and-globally-aware-network","slug":"ps2-net-a-locally-and-globally-aware-network","title":"PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation","date":"2019-08-15","arxiv_id":"1908.05425","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-semantic-segmentation-with","slug":"semi-supervised-semantic-segmentation-with","title":"Semi-Supervised Semantic Segmentation with High- and Low-level Consistency","date":"2019-08-15","arxiv_id":"1908.05724","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/semi-supervised-semantic-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/1908.05724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.05724"}},"official":{"repos":["sud0301/semisup-semseg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/explicit-shape-encoding-for-real-time","slug":"explicit-shape-encoding-for-real-time","title":"Explicit Shape Encoding for Real-Time Instance Segmentation","date":"2019-08-12","arxiv_id":"1908.04067","repositories_listed":1,"syntology":null},{"url":"/paper/improving-robustness-of-deep-learning-based","slug":"improving-robustness-of-deep-learning-based","title":"Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation","date":"2019-08-12","arxiv_id":"1908.04126","repositories_listed":1,"syntology":null},{"url":"/paper/nuclei-segmentation-via-a-deep-panoptic-model","slug":"nuclei-segmentation-via-a-deep-panoptic-model","title":"Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion","date":"2019-08-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/constrained-domain-adaptation-for","slug":"constrained-domain-adaptation-for","title":"Constrained domain adaptation for Image segmentation","date":"2019-08-08","arxiv_id":"1908.02996","repositories_listed":1,"syntology":null},{"url":"/paper/an-attempt-at-beating-the-3d-u-net","slug":"an-attempt-at-beating-the-3d-u-net","title":"An attempt at beating the 3D U-Net","date":"2019-08-06","arxiv_id":"1908.02182","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-microvascular-image-segmentation","slug":"unsupervised-microvascular-image-segmentation","title":"Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network","date":"2019-08-04","arxiv_id":"1908.01373","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-microvascular-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/1908.01373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01373"}},"official":{"repos":["shirgur/UMIS"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-unified-point-based-framework-for-3d","slug":"a-unified-point-based-framework-for-3d","title":"A Unified Point-Based Framework for 3D Segmentation","date":"2019-08-01","arxiv_id":"1908.00478","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-networks-for-low","slug":"convolutional-neural-networks-for-low","title":"Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/2d-and-3d-segmentation-of-uncertain-local","slug":"2d-and-3d-segmentation-of-uncertain-local","title":"2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy","date":"2019-07-30","arxiv_id":"1907.12868","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-architectures-for-generalized","slug":"deep-learning-architectures-for-generalized","title":"Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation","date":"2019-07-30","arxiv_id":"1907.12975","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-adversarial-examples-on-deep","slug":"impact-of-adversarial-examples-on-deep","title":"Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation","date":"2019-07-30","arxiv_id":"1907.13124","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/impact-of-adversarial-examples-on-deep#ran","syntology_url":"https://syntology.ai/paper/1907.13124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.13124"}},"official":{"repos":["utkuozbulak/adaptive-segmentation-mask-attack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fss-1000-a-1000-class-dataset-for-few-shot","slug":"fss-1000-a-1000-class-dataset-for-few-shot","title":"FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation","date":"2019-07-29","arxiv_id":"1907.12347","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-attention-based-semi-supervised","slug":"multi-task-attention-based-semi-supervised","title":"Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation","date":"2019-07-29","arxiv_id":"1907.12303","repositories_listed":1,"syntology":null},{"url":"/paper/quadtree-generating-networks-efficient","slug":"quadtree-generating-networks-efficient","title":"Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions","date":"2019-07-27","arxiv_id":"1907.11821","repositories_listed":1,"syntology":null},{"url":"/paper/grape-detection-segmentation-and-tracking","slug":"grape-detection-segmentation-and-tracking","title":"Grape detection, segmentation and tracking using deep neural networks and three-dimensional association","date":"2019-07-26","arxiv_id":"1907.11819","repositories_listed":1,"syntology":null},{"url":"/paper/cross-attention-network-for-semantic","slug":"cross-attention-network-for-semantic","title":"Cross Attention Network for Semantic Segmentation","date":"2019-07-25","arxiv_id":"1907.10958","repositories_listed":1,"syntology":null},{"url":"/paper/et-net-a-generic-edge-attention-guidance","slug":"et-net-a-generic-edge-attention-guidance","title":"ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation","date":"2019-07-25","arxiv_id":"1907.10936","repositories_listed":1,"syntology":null},{"url":"/paper/hetero-modal-variational-encoder-decoder-for","slug":"hetero-modal-variational-encoder-decoder-for","title":"Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation","date":"2019-07-25","arxiv_id":"1907.11150","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hetero-modal-variational-encoder-decoder-for#ran","syntology_url":"https://syntology.ai/paper/1907.11150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.11150"}},"official":{"repos":["ReubenDo/U-HVED"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/importance-aware-semantic-segmentation-with","slug":"importance-aware-semantic-segmentation-with","title":"Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems","date":"2019-07-25","arxiv_id":"1907.11066","repositories_listed":1,"syntology":null},{"url":"/paper/nodulenet-decoupled-false-positive","slug":"nodulenet-decoupled-false-positive","title":"NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation","date":"2019-07-25","arxiv_id":"1907.11320","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/nodulenet-decoupled-false-positive#ran","syntology_url":"https://syntology.ai/paper/1907.11320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.11320"}},"official":{"repos":["uci-cbcl/NoduleNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/overfitting-of-neural-nets-under-class","slug":"overfitting-of-neural-nets-under-class","title":"Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation","date":"2019-07-25","arxiv_id":"1907.10982","repositories_listed":1,"syntology":{"n":17,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/overfitting-of-neural-nets-under-class#ran","syntology_url":"https://syntology.ai/paper/1907.10982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10982"}},"official":{"repos":["ZerojumpLine/OverfittingUnderClassImbalance"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-domain-adaptation-for","slug":"self-supervised-domain-adaptation-for","title":"Self-supervised Domain Adaptation for Computer Vision Tasks","date":"2019-07-25","arxiv_id":"1907.10915","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/1907.10915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10915"}},"official":{"repos":["Jiaolong/self-supervised-da"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/segmenting-objects-in-day-and-nightedge","slug":"segmenting-objects-in-day-and-nightedge","title":"Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation","date":"2019-07-24","arxiv_id":"1907.10303","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-cell-instance-segmentation-with","slug":"multi-scale-cell-instance-segmentation-with","title":"Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes","date":"2019-07-22","arxiv_id":"1907.09140","repositories_listed":1,"syntology":null},{"url":"/paper/automated-muscle-segmentation-from-clinical","slug":"automated-muscle-segmentation-from-clinical","title":"Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling","date":"2019-07-21","arxiv_id":"1907.08915","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-segmentation-of-hyperspectral","slug":"unsupervised-segmentation-of-hyperspectral","title":"Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders","date":"2019-07-20","arxiv_id":"1907.08870","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-temporal-prior-from-motion-flow","slug":"incorporating-temporal-prior-from-motion-flow","title":"Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video","date":"2019-07-18","arxiv_id":"1907.07899","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/incorporating-temporal-prior-from-motion-flow#ran","syntology_url":"https://syntology.ai/paper/1907.07899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.07899"}},"official":{"repos":["keyuncheng/MF-TAPNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-segmentation-learning-downsampling","slug":"efficient-segmentation-learning-downsampling","title":"Efficient Segmentation: Learning Downsampling Near Semantic Boundaries","date":"2019-07-16","arxiv_id":"1907.07156","repositories_listed":1,"syntology":null},{"url":"/paper/separable-convolutional-lstms-for-faster","slug":"separable-convolutional-lstms-for-faster","title":"Separable Convolutional LSTMs for Faster Video Segmentation","date":"2019-07-16","arxiv_id":"1907.06876","repositories_listed":1,"syntology":null},{"url":"/paper/x-net-brain-stroke-lesion-segmentation-based","slug":"x-net-brain-stroke-lesion-segmentation-based","title":"X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies","date":"2019-07-16","arxiv_id":"1907.07000","repositories_listed":1,"syntology":null},{"url":"/paper/ca-refineneta-dual-input-wsi-image","slug":"ca-refineneta-dual-input-wsi-image","title":"DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention","date":"2019-07-15","arxiv_id":"1907.06358","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-tidemark-segmentation-in","slug":"deep-learning-for-tidemark-segmentation-in","title":"Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography","date":"2019-07-11","arxiv_id":"1907.05089","repositories_listed":1,"syntology":null},{"url":"/paper/unified-attentional-generative-adversarial","slug":"unified-attentional-generative-adversarial","title":"Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images","date":"2019-07-08","arxiv_id":"1907.03548","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-reliability-and-challenges-of","slug":"assessing-reliability-and-challenges-of","title":"Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation","date":"2019-07-07","arxiv_id":"1907.03338","repositories_listed":1,"syntology":null},{"url":"/paper/cardiac-mri-segmentation-with-strong","slug":"cardiac-mri-segmentation-with-strong","title":"Cardiac MRI Segmentation with Strong Anatomical Guarantees","date":"2019-07-05","arxiv_id":"1907.02865","repositories_listed":1,"syntology":null},{"url":"/paper/feature-based-image-clustering-and","slug":"feature-based-image-clustering-and","title":"Feature-Based Image Clustering and Segmentation Using Wavelets","date":"2019-07-05","arxiv_id":"1907.03591","repositories_listed":1,"syntology":null},{"url":"/paper/deep-attentive-features-for-prostate","slug":"deep-attentive-features-for-prostate","title":"Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound","date":"2019-07-03","arxiv_id":"1907.01743","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-semantic-segmentation-of","slug":"deep-learning-based-semantic-segmentation-of","title":"Deep Learning-Based Semantic Segmentation of Microscale Objects","date":"2019-07-03","arxiv_id":"1907.03576","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-uncertainty-quantification-for","slug":"supervised-uncertainty-quantification-for","title":"Supervised Uncertainty Quantification for Segmentation with Multiple Annotations","date":"2019-07-03","arxiv_id":"1907.01949","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/supervised-uncertainty-quantification-for#ran","syntology_url":"https://syntology.ai/paper/1907.01949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.01949"}},"official":null}},{"url":"/paper/improving-the-generalizability-of","slug":"improving-the-generalizability-of","title":"Improving the generalizability of convolutional neural network-based segmentation on CMR images","date":"2019-07-02","arxiv_id":"1907.01268","repositories_listed":1,"syntology":null},{"url":"/paper/proposal-tracking-and-segmentation-pts-a","slug":"proposal-tracking-and-segmentation-pts-a","title":"Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation","date":"2019-07-02","arxiv_id":"1907.01203","repositories_listed":1,"syntology":null},{"url":"/paper/chinese-relation-extraction-with-multi","slug":"chinese-relation-extraction-with-multi","title":"Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mobilegan-skin-lesion-segmentation-using-a","slug":"mobilegan-skin-lesion-segmentation-using-a","title":"SLSNet: Skin lesion segmentation using a lightweight generative adversarial network","date":"2019-07-01","arxiv_id":"1907.00856","repositories_listed":1,"syntology":null},{"url":"/paper/permutohedral-attention-module-for-efficient","slug":"permutohedral-attention-module-for-efficient","title":"Permutohedral Attention Module for Efficient Non-Local Neural Networks","date":"2019-07-01","arxiv_id":"1907.00641","repositories_listed":1,"syntology":null},{"url":"/paper/learning-where-to-look-while-tracking","slug":"learning-where-to-look-while-tracking","title":"Learning Where to Look While Tracking Instruments in Robot-assisted Surgery","date":"2019-06-29","arxiv_id":"1907.00214","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automatic-computer-aided-mass-detection","slug":"fully-automatic-computer-aided-mass-detection","title":"Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN","date":"2019-06-28","arxiv_id":"1906.12118","repositories_listed":1,"syntology":null},{"url":"/paper/multi-criteria-chinese-word-segmentation-with","slug":"multi-criteria-chinese-word-segmentation-with","title":"A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder","date":"2019-06-28","arxiv_id":"1906.12035","repositories_listed":1,"syntology":null},{"url":"/paper/hard-pixels-mining-learning-using-privileged","slug":"hard-pixels-mining-learning-using-privileged","title":"Hard Pixel Mining for Depth Privileged Semantic Segmentation","date":"2019-06-27","arxiv_id":"1906.11437","repositories_listed":1,"syntology":null}],"record_sha256":"e582c074ead31bc8ccf0713a7109818c4daa94450df308e4387e0960dfc66172","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}