{"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/13","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":13,"pages_in_order":148,"rows_per_page":100,"rows":[1201,1300],"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/12","next":"/task/semantic-segmentation/papers/14","papers":[{"url":"/paper/dada-depth-aware-domain-adaptation-in","slug":"dada-depth-aware-domain-adaptation-in","title":"DADA: Depth-aware Domain Adaptation in Semantic Segmentation","date":"2019-04-03","arxiv_id":"1904.01886","repositories_listed":2,"syntology":null},{"url":"/paper/dfanet-deep-feature-aggregation-for-real-time","slug":"dfanet-deep-feature-aggregation-for-real-time","title":"DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation","date":"2019-04-03","arxiv_id":"1904.02216","repositories_listed":2,"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/dfanet-deep-feature-aggregation-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1904.02216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02216"}},"official":null}},{"url":"/paper/gff-gated-fully-fusion-for-semantic","slug":"gff-gated-fully-fusion-for-semantic","title":"GFF: Gated Fully Fusion for Semantic Segmentation","date":"2019-04-03","arxiv_id":"1904.01803","repositories_listed":2,"syntology":null},{"url":"/paper/point-cloud-oversegmentation-with-graph","slug":"point-cloud-oversegmentation-with-graph","title":"Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning","date":"2019-04-03","arxiv_id":"1904.02113","repositories_listed":2,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":12,"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, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/point-cloud-oversegmentation-with-graph#ran","syntology_url":"https://syntology.ai/paper/1904.02113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02113"}},"official":{"repos":["loicland/superpoint_graph"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/multigrid-predictive-filter-flow-for","slug":"multigrid-predictive-filter-flow-for","title":"Multigrid Predictive Filter Flow for Unsupervised Learning on Videos","date":"2019-04-02","arxiv_id":"1904.01693","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multigrid-predictive-filter-flow-for#ran","syntology_url":"https://syntology.ai/paper/1904.01693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01693"}},"official":null}},{"url":"/paper/model-vulnerability-to-distributional-shifts","slug":"model-vulnerability-to-distributional-shifts","title":"Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets","date":"2019-03-28","arxiv_id":"1903.11900","repositories_listed":2,"syntology":null},{"url":"/paper/pyramid-mask-text-detector","slug":"pyramid-mask-text-detector","title":"Pyramid Mask Text Detector","date":"2019-03-28","arxiv_id":"1903.11800","repositories_listed":2,"syntology":null},{"url":"/paper/tensormask-a-foundation-for-dense-object","slug":"tensormask-a-foundation-for-dense-object","title":"TensorMask: A Foundation for Dense Object Segmentation","date":"2019-03-28","arxiv_id":"1903.12174","repositories_listed":2,"syntology":null},{"url":"/paper/deep-co-training-for-semi-supervised-image-2","slug":"deep-co-training-for-semi-supervised-image-2","title":"Deep Co-Training for Semi-Supervised Image Segmentation","date":"2019-03-27","arxiv_id":"1903.11233","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/deep-co-training-for-semi-supervised-image-2#ran","syntology_url":"https://syntology.ai/paper/1903.11233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.11233"}},"official":null}},{"url":"/paper/sliced-wasserstein-discrepancy-for","slug":"sliced-wasserstein-discrepancy-for","title":"Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation","date":"2019-03-10","arxiv_id":"1903.04064","repositories_listed":2,"syntology":null},{"url":"/paper/partial-order-pruning-for-best-speedaccuracy","slug":"partial-order-pruning-for-best-speedaccuracy","title":"Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search","date":"2019-03-09","arxiv_id":"1903.03777","repositories_listed":2,"syntology":null},{"url":"/paper/object-counting-and-instance-segmentation","slug":"object-counting-and-instance-segmentation","title":"Object Counting and Instance Segmentation with Image-level Supervision","date":"2019-03-06","arxiv_id":"1903.02494","repositories_listed":2,"syntology":null},{"url":"/paper/data-augmentation-using-learned-transforms","slug":"data-augmentation-using-learned-transforms","title":"Data augmentation using learned transformations for one-shot medical image segmentation","date":"2019-02-25","arxiv_id":"1902.09383","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/data-augmentation-using-learned-transforms#ran","syntology_url":"https://syntology.ai/paper/1902.09383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09383"}},"official":{"repos":["xamyzhao/brainstorm"],"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":["listed","official"]}}},{"url":"/paper/freelabel-a-publicly-available-annotation","slug":"freelabel-a-publicly-available-annotation","title":"FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces","date":"2019-02-18","arxiv_id":"1902.06806","repositories_listed":2,"syntology":null},{"url":"/paper/gauge-equivariant-convolutional-networks-and","slug":"gauge-equivariant-convolutional-networks-and","title":"Gauge Equivariant Convolutional Networks and the Icosahedral CNN","date":"2019-02-11","arxiv_id":"1902.04615","repositories_listed":2,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gauge-equivariant-convolutional-networks-and#ran","syntology_url":"https://syntology.ai/paper/1902.04615","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04615"}},"official":null}},{"url":"/paper/super-realtime-facial-landmark-detection-and","slug":"super-realtime-facial-landmark-detection-and","title":"Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters","date":"2019-02-09","arxiv_id":"1902.03459","repositories_listed":2,"syntology":null},{"url":"/paper/squeeze-excite-guided-few-shot-segmentation","slug":"squeeze-excite-guided-few-shot-segmentation","title":"'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images","date":"2019-02-04","arxiv_id":"1902.01314","repositories_listed":2,"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":0,"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/squeeze-excite-guided-few-shot-segmentation#ran","syntology_url":"https://syntology.ai/paper/1902.01314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01314"}},"official":{"repos":["abhi4ssj/few-shot-segmentation"],"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/optimized-high-resolution-3d-dense-u-net","slug":"optimized-high-resolution-3d-dense-u-net","title":"Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation","date":"2019-01-25","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/river-ice-segmentation-with-deep-learning","slug":"river-ice-segmentation-with-deep-learning","title":"River Ice Segmentation with Deep Learning","date":"2019-01-14","arxiv_id":"1901.04412","repositories_listed":2,"syntology":null},{"url":"/paper/boundary-aware-network-for-fast-and-high","slug":"boundary-aware-network-for-fast-and-high","title":"Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation","date":"2019-01-12","arxiv_id":"1901.03814","repositories_listed":2,"syntology":null},{"url":"/paper/post-mortem-iris-recognition-with-deep","slug":"post-mortem-iris-recognition-with-deep","title":"Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation","date":"2019-01-07","arxiv_id":"1901.01708","repositories_listed":2,"syntology":null},{"url":"/paper/a-curriculum-domain-adaptation-approach-to","slug":"a-curriculum-domain-adaptation-approach-to","title":"A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes","date":"2018-12-24","arxiv_id":"1812.09953","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/a-curriculum-domain-adaptation-approach-to#ran","syntology_url":"https://syntology.ai/paper/1812.09953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.09953"}},"official":{"repos":["YangZhang4065/AdaptationSeg"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/picanet-pixel-wise-contextual-attention","slug":"picanet-pixel-wise-contextual-attention","title":"PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection","date":"2018-12-15","arxiv_id":"1812.06314","repositories_listed":2,"syntology":null},{"url":"/paper/concentrated-comprehensive-convolutions-for","slug":"concentrated-comprehensive-convolutions-for","title":"C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation","date":"2018-12-12","arxiv_id":"1812.04920","repositories_listed":2,"syntology":null},{"url":"/paper/elastic-boundary-projection-for-3d-medical","slug":"elastic-boundary-projection-for-3d-medical","title":"Elastic Boundary Projection for 3D Medical Image Segmentation","date":"2018-12-03","arxiv_id":"1812.00518","repositories_listed":2,"syntology":null},{"url":"/paper/xnet-a-convolutional-neural-network-cnn","slug":"xnet-a-convolutional-neural-network-cnn","title":"XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets","date":"2018-12-03","arxiv_id":"1812.00548","repositories_listed":2,"syntology":null},{"url":"/paper/deepflux-for-skeletons-in-the-wild","slug":"deepflux-for-skeletons-in-the-wild","title":"DeepFlux for Skeletons in the Wild","date":"2018-11-30","arxiv_id":"1811.12608","repositories_listed":2,"syntology":null},{"url":"/paper/idd-a-dataset-for-exploring-problems-of","slug":"idd-a-dataset-for-exploring-problems-of","title":"IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments","date":"2018-11-26","arxiv_id":"1811.10200","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/idd-a-dataset-for-exploring-problems-of#ran","syntology_url":"https://syntology.ai/paper/1811.10200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10200"}},"official":null}},{"url":"/paper/scene-text-detection-with-supervised-pyramid","slug":"scene-text-detection-with-supervised-pyramid","title":"Scene Text Detection with Supervised Pyramid Context Network","date":"2018-11-21","arxiv_id":"1811.08605","repositories_listed":2,"syntology":null},{"url":"/paper/spherephd-applying-cnns-on-a-spherical","slug":"spherephd-applying-cnns-on-a-spherical","title":"SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images","date":"2018-11-20","arxiv_id":"1811.08196","repositories_listed":2,"syntology":null},{"url":"/paper/slum-segmentation-and-change-detection-a-deep","slug":"slum-segmentation-and-change-detection-a-deep","title":"Slum Segmentation and Change Detection : A Deep Learning Approach","date":"2018-11-19","arxiv_id":"1811.07896","repositories_listed":2,"syntology":null},{"url":"/paper/tukey-inspired-video-object-segmentation","slug":"tukey-inspired-video-object-segmentation","title":"Tukey-Inspired Video Object Segmentation","date":"2018-11-19","arxiv_id":"1811.07958","repositories_listed":2,"syntology":null},{"url":"/paper/learning-to-steer-by-mimicking-features-from","slug":"learning-to-steer-by-mimicking-features-from","title":"Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks","date":"2018-11-07","arxiv_id":"1811.02759","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-steer-by-mimicking-features-from#ran","syntology_url":"https://syntology.ai/paper/1811.02759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02759"}},"official":null}},{"url":"/paper/hide-and-seek-a-data-augmentation-technique","slug":"hide-and-seek-a-data-augmentation-technique","title":"Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond","date":"2018-11-06","arxiv_id":"1811.02545","repositories_listed":2,"syntology":null},{"url":"/paper/fast-graph-cut-based-optimization-for","slug":"fast-graph-cut-based-optimization-for","title":"Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images","date":"2018-10-19","arxiv_id":"1810.08427","repositories_listed":2,"syntology":null},{"url":"/paper/unrealrox-an-extremely-photorealistic-virtual","slug":"unrealrox-an-extremely-photorealistic-virtual","title":"UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation","date":"2018-10-16","arxiv_id":"1810.06936","repositories_listed":2,"syntology":null},{"url":"/paper/lets-take-a-walk-on-superpixels-graphs","slug":"lets-take-a-walk-on-superpixels-graphs","title":"Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation","date":"2018-10-10","arxiv_id":"1810.04461","repositories_listed":2,"syntology":null},{"url":"/paper/light-weight-refinenet-for-real-time-semantic","slug":"light-weight-refinenet-for-real-time-semantic","title":"Light-Weight RefineNet for Real-Time Semantic Segmentation","date":"2018-10-08","arxiv_id":"1810.03272","repositories_listed":2,"syntology":null},{"url":"/paper/open-source-presentation-attack-detection","slug":"open-source-presentation-attack-detection","title":"Open Source Presentation Attack Detection Baseline for Iris Recognition","date":"2018-09-26","arxiv_id":"1809.10172","repositories_listed":2,"syntology":null},{"url":"/paper/faster-training-of-mask-r-cnn-by-focusing-on","slug":"faster-training-of-mask-r-cnn-by-focusing-on","title":"Faster Training of Mask R-CNN by Focusing on Instance Boundaries","date":"2018-09-19","arxiv_id":"1809.07069","repositories_listed":2,"syntology":null},{"url":"/paper/devil-in-the-details-towards-accurate-single","slug":"devil-in-the-details-towards-accurate-single","title":"Devil in the Details: Towards Accurate Single and Multiple Human Parsing","date":"2018-09-17","arxiv_id":"1809.05996","repositories_listed":2,"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/devil-in-the-details-towards-accurate-single#ran","syntology_url":"https://syntology.ai/paper/1809.05996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05996"}},"official":{"repos":["liutinglt/CE2P"],"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/cbinfer-exploiting-frame-to-frame-locality","slug":"cbinfer-exploiting-frame-to-frame-locality","title":"CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams","date":"2018-08-15","arxiv_id":"1808.05488","repositories_listed":2,"syntology":null},{"url":"/paper/sketchyscene-richly-annotated-scene-sketches","slug":"sketchyscene-richly-annotated-scene-sketches","title":"SketchyScene: Richly-Annotated Scene Sketches","date":"2018-08-07","arxiv_id":"1808.02473","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/sketchyscene-richly-annotated-scene-sketches#ran","syntology_url":"https://syntology.ai/paper/1808.02473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.02473"}},"official":{"repos":["SketchyScene/SketchyScene"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-random-fields-as-recurrent-neural","slug":"conditional-random-fields-as-recurrent-neural","title":"Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation","date":"2018-07-19","arxiv_id":"1807.07464","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conditional-random-fields-as-recurrent-neural#ran","syntology_url":"https://syntology.ai/paper/1807.07464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.07464"}},"official":{"repos":["MiguelMonteiro/CRFasRNNLayer","MiguelMonteiro/permutohedral_lattice"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-multimodal-instance-segmentation","slug":"dynamic-multimodal-instance-segmentation","title":"Dynamic Multimodal Instance Segmentation guided by natural language queries","date":"2018-07-06","arxiv_id":"1807.02257","repositories_listed":2,"syntology":null},{"url":"/paper/modanet-a-large-scale-street-fashion-dataset","slug":"modanet-a-large-scale-street-fashion-dataset","title":"ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations","date":"2018-07-03","arxiv_id":"1807.01394","repositories_listed":2,"syntology":null},{"url":"/paper/3d-roi-aware-u-net-for-accurate-and-efficient","slug":"3d-roi-aware-u-net-for-accurate-and-efficient","title":"3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation","date":"2018-06-27","arxiv_id":"1806.10342","repositories_listed":2,"syntology":null},{"url":"/paper/hgr-net-a-fusion-network-for-hand-gesture","slug":"hgr-net-a-fusion-network-for-hand-gesture","title":"HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition","date":"2018-06-14","arxiv_id":"1806.05653","repositories_listed":2,"syntology":null},{"url":"/paper/dsslic-deep-semantic-segmentation-based","slug":"dsslic-deep-semantic-segmentation-based","title":"DSSLIC: Deep Semantic Segmentation-based Layered Image Compression","date":"2018-06-08","arxiv_id":"1806.03348","repositories_listed":2,"syntology":null},{"url":"/paper/fast-and-accurate-online-video-object","slug":"fast-and-accurate-online-video-object","title":"Fast and Accurate Online Video Object Segmentation via Tracking Parts","date":"2018-06-06","arxiv_id":"1806.02323","repositories_listed":2,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/fast-and-accurate-online-video-object#ran","syntology_url":"https://syntology.ai/paper/1806.02323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02323"}},"official":{"repos":["JingchunCheng/FAVOS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/cfcm-segmentation-via-coarse-to-fine-context","slug":"cfcm-segmentation-via-coarse-to-fine-context","title":"CFCM: Segmentation via Coarse to Fine Context Memory","date":"2018-06-04","arxiv_id":"1806.01413","repositories_listed":2,"syntology":null},{"url":"/paper/fast-video-object-segmentation-by-reference","slug":"fast-video-object-segmentation-by-reference","title":"Fast Video Object Segmentation by Reference-Guided Mask Propagation","date":"2018-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/the-lovasz-softmax-loss-a-tractable-surrogate-1","slug":"the-lovasz-softmax-loss-a-tractable-surrogate-1","title":"The LovÃ¡sz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/contextnet-exploring-context-and-detail-for","slug":"contextnet-exploring-context-and-detail-for","title":"ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time","date":"2018-05-11","arxiv_id":"1805.04554","repositories_listed":2,"syntology":null},{"url":"/paper/learning-to-see-the-invisible-end-to-end","slug":"learning-to-see-the-invisible-end-to-end","title":"Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation","date":"2018-04-24","arxiv_id":"1804.08864","repositories_listed":2,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":10,"phrase":"12 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learning-to-see-the-invisible-end-to-end#ran","syntology_url":"https://syntology.ai/paper/1804.08864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08864"}},"official":null}},{"url":"/paper/detnet-a-backbone-network-for-object","slug":"detnet-a-backbone-network-for-object","title":"DetNet: A Backbone network for Object Detection","date":"2018-04-17","arxiv_id":"1804.06215","repositories_listed":2,"syntology":null},{"url":"/paper/understanding-humans-in-crowded-scenes-deep","slug":"understanding-humans-in-crowded-scenes-deep","title":"Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing","date":"2018-04-10","arxiv_id":"1804.03287","repositories_listed":2,"syntology":null},{"url":"/paper/megadepth-learning-single-view-depth","slug":"megadepth-learning-single-view-depth","title":"MegaDepth: Learning Single-View Depth Prediction from Internet Photos","date":"2018-04-02","arxiv_id":"1804.00607","repositories_listed":2,"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/megadepth-learning-single-view-depth#ran","syntology_url":"https://syntology.ai/paper/1804.00607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00607"}},"official":null}},{"url":"/paper/learning-pixel-level-semantic-affinity-with","slug":"learning-pixel-level-semantic-affinity-with","title":"Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation","date":"2018-03-28","arxiv_id":"1803.10464","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-pixel-level-semantic-affinity-with#ran","syntology_url":"https://syntology.ai/paper/1803.10464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10464"}},"official":{"repos":["jiwoon-ahn/psa"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deepscores-a-dataset-for-segmentation","slug":"deepscores-a-dataset-for-segmentation","title":"DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects","date":"2018-03-27","arxiv_id":"1804.00525","repositories_listed":2,"syntology":null},{"url":"/paper/learning-deep-structured-active-contours-end","slug":"learning-deep-structured-active-contours-end","title":"Learning deep structured active contours end-to-end","date":"2018-03-16","arxiv_id":"1803.06329","repositories_listed":2,"syntology":null},{"url":"/paper/the-apolloscape-open-dataset-for-autonomous","slug":"the-apolloscape-open-dataset-for-autonomous","title":"The ApolloScape Open Dataset for Autonomous Driving and its Application","date":"2018-03-16","arxiv_id":"1803.06184","repositories_listed":2,"syntology":null},{"url":"/paper/training-of-convolutional-networks-on","slug":"training-of-convolutional-networks-on","title":"Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation","date":"2018-03-15","arxiv_id":"1803.05675","repositories_listed":2,"syntology":null},{"url":"/paper/shuffleseg-real-time-semantic-segmentation","slug":"shuffleseg-real-time-semantic-segmentation","title":"ShuffleSeg: Real-time Semantic Segmentation Network","date":"2018-03-10","arxiv_id":"1803.03816","repositories_listed":2,"syntology":null},{"url":"/paper/rtseg-real-time-semantic-segmentation","slug":"rtseg-real-time-semantic-segmentation","title":"RTSeg: Real-time Semantic Segmentation Comparative Study","date":"2018-03-07","arxiv_id":"1803.02758","repositories_listed":2,"syntology":null},{"url":"/paper/automatic-instrument-segmentation-in-robot","slug":"automatic-instrument-segmentation-in-robot","title":"Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning","date":"2018-03-03","arxiv_id":"1803.01207","repositories_listed":2,"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/automatic-instrument-segmentation-in-robot#ran","syntology_url":"https://syntology.ai/paper/1803.01207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01207"}},"official":{"repos":["ternaus/robot-surgery-segmentation"],"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/tell-me-where-to-look-guided-attention","slug":"tell-me-where-to-look-guided-attention","title":"Tell Me Where to Look: Guided Attention Inference Network","date":"2018-02-27","arxiv_id":"1802.10171","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tell-me-where-to-look-guided-attention#ran","syntology_url":"https://syntology.ai/paper/1802.10171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10171"}},"official":null}},{"url":"/paper/bonnet-an-open-source-training-and-deployment","slug":"bonnet-an-open-source-training-and-deployment","title":"Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs","date":"2018-02-25","arxiv_id":"1802.08960","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/bonnet-an-open-source-training-and-deployment#ran","syntology_url":"https://syntology.ai/paper/1802.08960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08960"}},"official":{"repos":["PRBonn/bonnet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/splatnet-sparse-lattice-networks-for-point","slug":"splatnet-sparse-lattice-networks-for-point","title":"SPLATNet: Sparse Lattice Networks for Point Cloud Processing","date":"2018-02-22","arxiv_id":"1802.08275","repositories_listed":2,"syntology":null},{"url":"/paper/multiclass-weighted-loss-for-instance","slug":"multiclass-weighted-loss-for-instance","title":"Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells","date":"2018-02-21","arxiv_id":"1802.07465","repositories_listed":2,"syntology":null},{"url":"/paper/recurrent-slice-networks-for-3d-segmentation","slug":"recurrent-slice-networks-for-3d-segmentation","title":"Recurrent Slice Networks for 3D Segmentation of Point Clouds","date":"2018-02-13","arxiv_id":"1802.04402","repositories_listed":2,"syntology":null},{"url":"/paper/sbnet-sparse-blocks-network-for-fast","slug":"sbnet-sparse-blocks-network-for-fast","title":"SBNet: Sparse Blocks Network for Fast Inference","date":"2018-01-07","arxiv_id":"1801.02108","repositories_listed":2,"syntology":null},{"url":"/paper/recurrent-pixel-embedding-for-instance","slug":"recurrent-pixel-embedding-for-instance","title":"Recurrent Pixel Embedding for Instance Grouping","date":"2017-12-22","arxiv_id":"1712.08273","repositories_listed":2,"syntology":null},{"url":"/paper/towards-dense-object-tracking-in-a-2d","slug":"towards-dense-object-tracking-in-a-2d","title":"Towards dense object tracking in a 2D honeybee hive","date":"2017-12-22","arxiv_id":"1712.08324","repositories_listed":2,"syntology":null},{"url":"/paper/pointwise-convolutional-neural-networks","slug":"pointwise-convolutional-neural-networks","title":"Pointwise Convolutional Neural Networks","date":"2017-12-14","arxiv_id":"1712.05245","repositories_listed":2,"syntology":null},{"url":"/paper/in-place-activated-batchnorm-for-memory","slug":"in-place-activated-batchnorm-for-memory","title":"In-Place Activated BatchNorm for Memory-Optimized Training of DNNs","date":"2017-12-07","arxiv_id":"1712.02616","repositories_listed":2,"syntology":null},{"url":"/paper/spatially-adaptive-filter-units-for-deep","slug":"spatially-adaptive-filter-units-for-deep","title":"Spatially-Adaptive Filter Units for Deep Neural Networks","date":"2017-11-30","arxiv_id":"1711.11473","repositories_listed":2,"syntology":null},{"url":"/paper/large-scale-point-cloud-semantic-segmentation","slug":"large-scale-point-cloud-semantic-segmentation","title":"Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs","date":"2017-11-27","arxiv_id":"1711.09869","repositories_listed":2,"syntology":null},{"url":"/paper/cost-effective-active-learning-for-melanoma","slug":"cost-effective-active-learning-for-melanoma","title":"Cost-Effective Active Learning for Melanoma Segmentation","date":"2017-11-24","arxiv_id":"1711.09168","repositories_listed":2,"syntology":null},{"url":"/paper/deep-extreme-cut-from-extreme-points-to","slug":"deep-extreme-cut-from-extreme-points-to","title":"Deep Extreme Cut: From Extreme Points to Object Segmentation","date":"2017-11-24","arxiv_id":"1711.09081","repositories_listed":2,"syntology":null},{"url":"/paper/visda-the-visual-domain-adaptation-challenge","slug":"visda-the-visual-domain-adaptation-challenge","title":"VisDA: The Visual Domain Adaptation Challenge","date":"2017-10-18","arxiv_id":"1710.06924","repositories_listed":2,"syntology":null},{"url":"/paper/3d-graph-neural-networks-for-rgbd-semantic","slug":"3d-graph-neural-networks-for-rgbd-semantic","title":"3D Graph Neural Networks for RGBD Semantic Segmentation","date":"2017-10-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/h-denseunet-hybrid-densely-connected-unet-for","slug":"h-denseunet-hybrid-densely-connected-unet-for","title":"H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes","date":"2017-09-21","arxiv_id":"1709.07330","repositories_listed":2,"syntology":null},{"url":"/paper/deepunet-a-deep-fully-convolutional-network","slug":"deepunet-a-deep-fully-convolutional-network","title":"DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation","date":"2017-09-01","arxiv_id":"1709.00201","repositories_listed":2,"syntology":null},{"url":"/paper/chainercv-a-library-for-deep-learning-in","slug":"chainercv-a-library-for-deep-learning-in","title":"ChainerCV: a Library for Deep Learning in Computer Vision","date":"2017-08-28","arxiv_id":"1708.08169","repositories_listed":2,"syntology":null},{"url":"/paper/blitznet-a-real-time-deep-network-for-scene","slug":"blitznet-a-real-time-deep-network-for-scene","title":"BlitzNet: A Real-Time Deep Network for Scene Understanding","date":"2017-08-09","arxiv_id":"1708.02813","repositories_listed":2,"syntology":null},{"url":"/paper/learning-aerial-image-segmentation-from","slug":"learning-aerial-image-segmentation-from","title":"Learning Aerial Image Segmentation from Online Maps","date":"2017-07-21","arxiv_id":"1707.06879","repositories_listed":2,"syntology":null},{"url":"/paper/discovering-class-specific-pixels-for-weakly","slug":"discovering-class-specific-pixels-for-weakly","title":"Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation","date":"2017-07-18","arxiv_id":"1707.05821","repositories_listed":2,"syntology":null},{"url":"/paper/revisiting-unreasonable-effectiveness-of-data","slug":"revisiting-unreasonable-effectiveness-of-data","title":"Revisiting Unreasonable Effectiveness of Data in Deep Learning Era","date":"2017-07-10","arxiv_id":"1707.02968","repositories_listed":2,"syntology":null},{"url":"/paper/wildcat-weakly-supervised-learning-of-deep","slug":"wildcat-weakly-supervised-learning-of-deep","title":"WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/illuminating-pedestrians-via-simultaneous","slug":"illuminating-pedestrians-via-simultaneous","title":"Illuminating Pedestrians via Simultaneous Detection & Segmentation","date":"2017-06-26","arxiv_id":"1706.08564","repositories_listed":2,"syntology":null},{"url":"/paper/tversky-loss-function-for-image-segmentation","slug":"tversky-loss-function-for-image-segmentation","title":"Tversky loss function for image segmentation using 3D fully convolutional deep networks","date":"2017-06-18","arxiv_id":"1706.05721","repositories_listed":2,"syntology":null},{"url":"/paper/segan-adversarial-network-with-multi-scale","slug":"segan-adversarial-network-with-multi-scale","title":"SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation","date":"2017-06-06","arxiv_id":"1706.01805","repositories_listed":2,"syntology":null},{"url":"/paper/a-review-on-deep-learning-techniques-applied","slug":"a-review-on-deep-learning-techniques-applied","title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation","date":"2017-04-22","arxiv_id":"1704.06857","repositories_listed":2,"syntology":null},{"url":"/paper/annotating-object-instances-with-a-polygon","slug":"annotating-object-instances-with-a-polygon","title":"Annotating Object Instances with a Polygon-RNN","date":"2017-04-18","arxiv_id":"1704.05548","repositories_listed":2,"syntology":null},{"url":"/paper/relaynet-retinal-layer-and-fluid-segmentation","slug":"relaynet-retinal-layer-and-fluid-segmentation","title":"ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network","date":"2017-04-07","arxiv_id":"1704.02161","repositories_listed":2,"syntology":null},{"url":"/paper/adversarial-examples-for-semantic","slug":"adversarial-examples-for-semantic","title":"Adversarial Examples for Semantic Segmentation and Object Detection","date":"2017-03-24","arxiv_id":"1703.08603","repositories_listed":2,"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":0,"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/adversarial-examples-for-semantic#ran","syntology_url":"https://syntology.ai/paper/1703.08603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.08603"}},"official":null}},{"url":"/paper/predicting-deeper-into-the-future-of-semantic","slug":"predicting-deeper-into-the-future-of-semantic","title":"Predicting Deeper into the Future of Semantic Segmentation","date":"2017-03-22","arxiv_id":"1703.07684","repositories_listed":2,"syntology":null},{"url":"/paper/roomnet-end-to-end-room-layout-estimation","slug":"roomnet-end-to-end-room-layout-estimation","title":"RoomNet: End-to-End Room Layout Estimation","date":"2017-03-18","arxiv_id":"1703.06241","repositories_listed":2,"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/roomnet-end-to-end-room-layout-estimation#ran","syntology_url":"https://syntology.ai/paper/1703.06241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06241"}},"official":null}},{"url":"/paper/large-kernel-matters-improve-semantic","slug":"large-kernel-matters-improve-semantic","title":"Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network","date":"2017-03-08","arxiv_id":"1703.02719","repositories_listed":2,"syntology":null}],"record_sha256":"1c2f18bc0fa408323d173a5ef722c2f5e553ad5aac4567c3a31007ca1b158f74","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}