{"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/63","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":63,"pages_in_order":148,"rows_per_page":100,"rows":[6201,6300],"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/62","next":"/task/semantic-segmentation/papers/64","papers":[{"url":"/paper/mask-based-unsupervised-content-transfer","slug":"mask-based-unsupervised-content-transfer","title":"Mask Based Unsupervised Content Transfer","date":"2019-06-15","arxiv_id":"1906.06558","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mask-based-unsupervised-content-transfer#ran","syntology_url":"https://syntology.ai/paper/1906.06558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.06558"}},"official":{"repos":["rmokady/mbu-content-tansfer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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/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/mimic-and-fool-a-task-agnostic-adversarial","slug":"mimic-and-fool-a-task-agnostic-adversarial","title":"Mimic and Fool: A Task Agnostic Adversarial Attack","date":"2019-06-11","arxiv_id":"1906.04606","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-change-detection-for-high-1","slug":"end-to-end-change-detection-for-high-1","title":"End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++","date":"2019-06-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-view-semantic-segmentation-for-sensing","slug":"cross-view-semantic-segmentation-for-sensing","title":"Cross-view Semantic Segmentation for Sensing Surroundings","date":"2019-06-09","arxiv_id":"1906.03560","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/salient-building-outline-enhancement-and","slug":"salient-building-outline-enhancement-and","title":"Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing","date":"2019-06-06","arxiv_id":"1906.02426","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/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/evaluating-scalable-bayesian-deep-learning","slug":"evaluating-scalable-bayesian-deep-learning","title":"Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision","date":"2019-06-04","arxiv_id":"1906.01620","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluating-scalable-bayesian-deep-learning#ran","syntology_url":"https://syntology.ai/paper/1906.01620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01620"}},"official":{"repos":["fregu856/evaluating_bdl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-object-bounding-boxes-for-3d","slug":"learning-object-bounding-boxes-for-3d","title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","date":"2019-06-04","arxiv_id":"1906.01140","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-object-bounding-boxes-for-3d#ran","syntology_url":"https://syntology.ai/paper/1906.01140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01140"}},"official":{"repos":["Yang7879/3D-BoNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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/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/detecting-anomalies-in-image-classification","slug":"detecting-anomalies-in-image-classification","title":"Detecting Anomalies in Image Classification by Means of Semantic Relationships","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/event-cameras-contrast-maximization-and","slug":"event-cameras-contrast-maximization-and","title":"Event Cameras, Contrast Maximization and Reward Functions: An Analysis","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/in-defense-of-pre-trained-imagenet-1","slug":"in-defense-of-pre-trained-imagenet-1","title":"In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving Images","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/scene-parsing-via-integrated-classification","slug":"scene-parsing-via-integrated-classification","title":"Scene Parsing via Integrated Classification Model and Variance-Based Regularization","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/semantic-segmentation-of-crop-type-in-africa","slug":"semantic-segmentation-of-crop-type-in-africa","title":"Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods","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/emergence-of-object-segmentation-in-perturbed","slug":"emergence-of-object-segmentation-in-perturbed","title":"Emergence of Object Segmentation in Perturbed Generative Models","date":"2019-05-29","arxiv_id":"1905.12663","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/vision-based-robotic-grasping-from-object","slug":"vision-based-robotic-grasping-from-object","title":"Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review","date":"2019-05-16","arxiv_id":"1905.06658","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/online-normalization-for-training-neural","slug":"online-normalization-for-training-neural","title":"Online Normalization for Training Neural Networks","date":"2019-05-15","arxiv_id":"1905.05894","repositories_listed":1,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/online-normalization-for-training-neural#ran","syntology_url":"https://syntology.ai/paper/1905.05894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.05894"}},"official":{"repos":["cerebras/online-normalization"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/budgeted-training-rethinking-deep-neural","slug":"budgeted-training-rethinking-deep-neural","title":"Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints","date":"2019-05-12","arxiv_id":"1905.04753","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/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/spatially-constrained-generative-adversarial","slug":"spatially-constrained-generative-adversarial","title":"Spatially Constrained GAN for Face and Fashion Synthesis","date":"2019-05-07","arxiv_id":"1905.02320","repositories_listed":1,"syntology":null},{"url":"/paper/deep-visual-city-recognition-visualization","slug":"deep-visual-city-recognition-visualization","title":"Deep Visual City Recognition Visualization","date":"2019-05-06","arxiv_id":"1905.01932","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/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/instance-aware-image-to-image-translation","slug":"instance-aware-image-to-image-translation","title":"Instance-aware Image-to-Image Translation","date":"2019-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-the-use-of-arxiv-as-a-dataset","slug":"on-the-use-of-arxiv-as-a-dataset","title":"On the Use of ArXiv as a Dataset","date":"2019-04-30","arxiv_id":"1905.00075","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-the-use-of-arxiv-as-a-dataset#ran","syntology_url":"https://syntology.ai/paper/1905.00075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.00075"}},"official":{"repos":["mattbierbaum/arxiv-public-datasets"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/box-driven-class-wise-region-masking-and","slug":"box-driven-class-wise-region-masking-and","title":"Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation","date":"2019-04-26","arxiv_id":"1904.11693","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/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/switchable-whitening-for-deep-representation","slug":"switchable-whitening-for-deep-representation","title":"Switchable Whitening for Deep Representation Learning","date":"2019-04-22","arxiv_id":"1904.09739","repositories_listed":1,"syntology":{"n":10,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/switchable-whitening-for-deep-representation#ran","syntology_url":"https://syntology.ai/paper/1904.09739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09739"}},"official":{"repos":["XingangPan/Switchable-Whitening"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/190411953","slug":"190411953","title":"Temporal Unet: Sample Level Human Action Recognition using WiFi","date":"2019-04-19","arxiv_id":"1904.11953","repositories_listed":1,"syntology":null},{"url":"/paper/fast-single-image-dehazing-via-multilevel","slug":"fast-single-image-dehazing-via-multilevel","title":"Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization","date":"2019-04-18","arxiv_id":"1904.08573","repositories_listed":1,"syntology":null},{"url":"/paper/road-crack-detection-using-deep-convolutional","slug":"road-crack-detection-using-deep-convolutional","title":"Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding","date":"2019-04-18","arxiv_id":"1904.08582","repositories_listed":1,"syntology":null},{"url":"/paper/190408141","slug":"190408141","title":"MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation","date":"2019-04-17","arxiv_id":"1904.08141","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/190407934","slug":"190407934","title":"Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations","date":"2019-04-16","arxiv_id":"1904.07934","repositories_listed":1,"syntology":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/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/learning-shape-templates-with-structured","slug":"learning-shape-templates-with-structured","title":"Learning Shape Templates with Structured Implicit Functions","date":"2019-04-12","arxiv_id":"1904.06447","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/cross-modal-self-attention-network-for","slug":"cross-modal-self-attention-network-for","title":"Cross-Modal Self-Attention Network for Referring Image Segmentation","date":"2019-04-09","arxiv_id":"1904.04745","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/from-patch-to-image-segmentation-using-fully","slug":"from-patch-to-image-segmentation-using-fully","title":"From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images","date":"2019-04-08","arxiv_id":"1904.03892","repositories_listed":1,"syntology":null},{"url":"/paper/log-barrier-constrained-cnns","slug":"log-barrier-constrained-cnns","title":"Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions","date":"2019-04-08","arxiv_id":"1904.04205","repositories_listed":1,"syntology":null},{"url":"/paper/pushing-the-right-boundaries-matters","slug":"pushing-the-right-boundaries-matters","title":"Wasserstein Adversarial Regularization (WAR) on label noise","date":"2019-04-08","arxiv_id":"1904.03936","repositories_listed":1,"syntology":null},{"url":"/paper/can-gcns-go-as-deep-as-cnns","slug":"can-gcns-go-as-deep-as-cnns","title":"DeepGCNs: Can GCNs Go as Deep as CNNs?","date":"2019-04-07","arxiv_id":"1904.03751","repositories_listed":1,"syntology":null},{"url":"/paper/shapemask-learning-to-segment-novel-objects","slug":"shapemask-learning-to-segment-novel-objects","title":"ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors","date":"2019-04-05","arxiv_id":"1904.03239","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/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/template-based-automatic-search-of-compact","slug":"template-based-automatic-search-of-compact","title":"Template-Based Automatic Search of Compact Semantic Segmentation Architectures","date":"2019-04-04","arxiv_id":"1904.02365","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/defectnet-multi-class-fault-detection-on","slug":"defectnet-multi-class-fault-detection-on","title":"DefectNET: multi-class fault detection on highly-imbalanced datasets","date":"2019-04-01","arxiv_id":"1904.00863","repositories_listed":1,"syntology":null},{"url":"/paper/regional-homogeneity-towards-learning","slug":"regional-homogeneity-towards-learning","title":"Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses","date":"2019-04-01","arxiv_id":"1904.00979","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/regional-homogeneity-towards-learning#ran","syntology_url":"https://syntology.ai/paper/1904.00979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00979"}},"official":{"repos":["LiYingwei/Regional-Homogeneity"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-occupancy-grid-learning-from","slug":"self-supervised-occupancy-grid-learning-from","title":"Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation","date":"2019-03-31","arxiv_id":"1904.00415","repositories_listed":1,"syntology":null},{"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/self-supervised-learning-via-conditional","slug":"self-supervised-learning-via-conditional","title":"Self-Supervised Learning via Conditional Motion Propagation","date":"2019-03-27","arxiv_id":"1903.11412","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-via-conditional#ran","syntology_url":"https://syntology.ai/paper/1903.11412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.11412"}},"official":{"repos":["XiaohangZhan/conditional-motion-propagation"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"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/scale-adaptive-neural-dense-features-learning","slug":"scale-adaptive-neural-dense-features-learning","title":"Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation","date":"2019-03-25","arxiv_id":"1903.10427","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/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/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/learning-correspondence-from-the-cycle","slug":"learning-correspondence-from-the-cycle","title":"Learning Correspondence from the Cycle-Consistency of Time","date":"2019-03-18","arxiv_id":"1903.07593","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/neural-scene-decomposition-for-multi-person","slug":"neural-scene-decomposition-for-multi-person","title":"Neural Scene Decomposition for Multi-Person Motion Capture","date":"2019-03-13","arxiv_id":"1903.05684","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}],"record_sha256":"8324a034fa46cf7db89e50b99636bb6dcc4186f743c8320f934a69b3b379c132","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}