{"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/52","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":52,"pages_in_order":131,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/task/segmentation/papers/53","papers":[{"url":"/paper/end-to-end-detection-segmentation-network","slug":"end-to-end-detection-segmentation-network","title":"End-to-end detection-segmentation network with ROI convolution","date":"2018-01-08","arxiv_id":"1801.02722","repositories_listed":1,"syntology":null},{"url":"/paper/detection-and-segmentation-of-the-left","slug":"detection-and-segmentation-of-the-left","title":"Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning","date":"2018-01-07","arxiv_id":"1801.02171","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-grad-gan-for-virtual-to-real","slug":"semantic-aware-grad-gan-for-virtual-to-real","title":"Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption","date":"2018-01-05","arxiv_id":"1801.01726","repositories_listed":1,"syntology":null},{"url":"/paper/dense-fully-convolutional-network-for-skin","slug":"dense-fully-convolutional-network-for-skin","title":"Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation","date":"2017-12-29","arxiv_id":"1712.10207","repositories_listed":1,"syntology":null},{"url":"/paper/brain-tumor-segmentation-based-on-refined","slug":"brain-tumor-segmentation-based-on-refined","title":"Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss","date":"2017-12-25","arxiv_id":"1712.09093","repositories_listed":1,"syntology":null},{"url":"/paper/dual-long-short-term-memory-networks-for-sub","slug":"dual-long-short-term-memory-networks-for-sub","title":"Dual Long Short-Term Memory Networks for Sub-Character Representation Learning","date":"2017-12-23","arxiv_id":"1712.08841","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-synthesis-learning-enables","slug":"adversarial-synthesis-learning-enables","title":"Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth","date":"2017-12-20","arxiv_id":"1712.07695","repositories_listed":1,"syntology":null},{"url":"/paper/deep-cnn-ensembles-and-suggestive-annotations","slug":"deep-cnn-ensembles-and-suggestive-annotations","title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","date":"2017-12-14","arxiv_id":"1712.05319","repositories_listed":1,"syntology":null},{"url":"/paper/a-multimodal-corpus-of-expert-gaze-and","slug":"a-multimodal-corpus-of-expert-gaze-and","title":"A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks","date":"2017-12-13","arxiv_id":"1712.04798","repositories_listed":1,"syntology":null},{"url":"/paper/per-pixel-feedback-for-improving-semantic","slug":"per-pixel-feedback-for-improving-semantic","title":"Per-Pixel Feedback for improving Semantic Segmentation","date":"2017-12-07","arxiv_id":"1712.02861","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-networks-for-semantic","slug":"recurrent-neural-networks-for-semantic","title":"Recurrent Neural Networks for Semantic Instance Segmentation","date":"2017-12-02","arxiv_id":"1712.00617","repositories_listed":1,"syntology":null},{"url":"/paper/splenomegaly-segmentation-using-global","slug":"splenomegaly-segmentation-using-global","title":"Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks","date":"2017-12-02","arxiv_id":"1712.00542","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-robustness-of-semantic-segmentation","slug":"on-the-robustness-of-semantic-segmentation","title":"On the Robustness of Semantic Segmentation Models to Adversarial Attacks","date":"2017-11-27","arxiv_id":"1711.09856","repositories_listed":1,"syntology":null},{"url":"/paper/distance-to-center-of-mass-encoding-for","slug":"distance-to-center-of-mass-encoding-for","title":"Distance to Center of Mass Encoding for Instance Segmentation","date":"2017-11-24","arxiv_id":"1711.09060","repositories_listed":1,"syntology":null},{"url":"/paper/sgpn-similarity-group-proposal-network-for-3d","slug":"sgpn-similarity-group-proposal-network-for-3d","title":"SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation","date":"2017-11-23","arxiv_id":"1711.08588","repositories_listed":1,"syntology":null},{"url":"/paper/s4net-single-stage-salient-instance","slug":"s4net-single-stage-salient-instance","title":"S4Net: Single Stage Salient-Instance Segmentation","date":"2017-11-21","arxiv_id":"1711.07618","repositories_listed":1,"syntology":null},{"url":"/paper/language-based-image-editing-with-recurrent","slug":"language-based-image-editing-with-recurrent","title":"Language-Based Image Editing with Recurrent Attentive Models","date":"2017-11-16","arxiv_id":"1711.06288","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/language-based-image-editing-with-recurrent#ran","syntology_url":"https://syntology.ai/paper/1711.06288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06288"}},"official":{"repos":["Jianbo-Lab/LBIE"],"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/minimizing-supervision-for-free-space","slug":"minimizing-supervision-for-free-space","title":"Minimizing Supervision for Free-space Segmentation","date":"2017-11-16","arxiv_id":"1711.05998","repositories_listed":1,"syntology":null},{"url":"/paper/priming-neural-networks","slug":"priming-neural-networks","title":"Priming Neural Networks","date":"2017-11-16","arxiv_id":"1711.05918","repositories_listed":1,"syntology":null},{"url":"/paper/squeeze-segnet-a-new-fast-deep-convolutional","slug":"squeeze-segnet-a-new-fast-deep-convolutional","title":"Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation","date":"2017-11-15","arxiv_id":"1711.05491","repositories_listed":1,"syntology":null},{"url":"/paper/guided-machine-learning-for-power-grid","slug":"guided-machine-learning-for-power-grid","title":"Guided Machine Learning for power grid segmentation","date":"2017-11-13","arxiv_id":"1711.09715","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-and-unsupervised-segmentation","slug":"supervised-and-unsupervised-segmentation","title":"Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut.","date":"2017-11-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-automatic-3d-shape-instantiation-for","slug":"towards-automatic-3d-shape-instantiation-for","title":"Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net","date":"2017-11-04","arxiv_id":"1711.01506","repositories_listed":1,"syntology":null},{"url":"/paper/axondeepseg-automatic-axon-and-myelin","slug":"axondeepseg-automatic-axon-and-myelin","title":"AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks","date":"2017-11-03","arxiv_id":"1711.01004","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-automatic-fetal-brain-extraction-in","slug":"real-time-automatic-fetal-brain-extraction-in","title":"Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning","date":"2017-10-25","arxiv_id":"1710.09338","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-deep-structured-nets-for-mass","slug":"adversarial-deep-structured-nets-for-mass","title":"Adversarial Deep Structured Nets for Mass Segmentation from Mammograms","date":"2017-10-24","arxiv_id":"1710.09288","repositories_listed":1,"syntology":null},{"url":"/paper/casict-tibetan-word-segmentation-system-for","slug":"casict-tibetan-word-segmentation-system-for","title":"CASICT Tibetan Word Segmentation System for MLWS2017","date":"2017-10-17","arxiv_id":"1710.06112","repositories_listed":1,"syntology":null},{"url":"/paper/isointense-infant-brain-segmentation-with-a","slug":"isointense-infant-brain-segmentation-with-a","title":"Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network","date":"2017-10-16","arxiv_id":"1710.05956","repositories_listed":1,"syntology":null},{"url":"/paper/spinal-cord-gray-matter-segmentation-using","slug":"spinal-cord-gray-matter-segmentation-using","title":"Spinal cord gray matter segmentation using deep dilated convolutions","date":"2017-10-02","arxiv_id":"1710.01269","repositories_listed":1,"syntology":null},{"url":"/paper/automated-sub-cortical-brain-structure","slug":"automated-sub-cortical-brain-structure","title":"Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features","date":"2017-09-26","arxiv_id":"1709.09075","repositories_listed":1,"syntology":null},{"url":"/paper/segflow-joint-learning-for-video-object","slug":"segflow-joint-learning-for-video-object","title":"SegFlow: Joint Learning for Video Object Segmentation and Optical Flow","date":"2017-09-20","arxiv_id":"1709.06750","repositories_listed":1,"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/segflow-joint-learning-for-video-object#ran","syntology_url":"https://syntology.ai/paper/1709.06750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.06750"}},"official":{"repos":["JingchunCheng/SegFlow"],"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/a-fast-and-accurate-vietnamese-word-segmenter","slug":"a-fast-and-accurate-vietnamese-word-segmenter","title":"A Fast and Accurate Vietnamese Word Segmenter","date":"2017-09-19","arxiv_id":"1709.06307","repositories_listed":1,"syntology":null},{"url":"/paper/microscopy-cell-segmentation-via-adversarial","slug":"microscopy-cell-segmentation-via-adversarial","title":"Microscopy Cell Segmentation via Adversarial Neural Networks","date":"2017-09-18","arxiv_id":"1709.05860","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-learning-for-segmentation-of","slug":"multi-task-learning-for-segmentation-of","title":"Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks","date":"2017-09-18","arxiv_id":"1709.05932","repositories_listed":1,"syntology":null},{"url":"/paper/3d-densely-convolutional-networks-for-1","slug":"3d-densely-convolutional-networks-for-1","title":"3D Densely Convolutional Networks for VolumetricSegmentation","date":"2017-09-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/3d-densely-convolutional-networks-for","slug":"3d-densely-convolutional-networks-for","title":"3D Densely Convolutional Networks for Volumetric Segmentation","date":"2017-09-11","arxiv_id":"1709.03199","repositories_listed":1,"syntology":null},{"url":"/paper/holistic-instance-level-human-parsing","slug":"holistic-instance-level-human-parsing","title":"Holistic, Instance-Level Human Parsing","date":"2017-09-11","arxiv_id":"1709.03612","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-segment-breast-biopsy-whole-slide","slug":"learning-to-segment-breast-biopsy-whole-slide","title":"Learning to Segment Breast Biopsy Whole Slide Images","date":"2017-09-08","arxiv_id":"1709.02554","repositories_listed":1,"syntology":null},{"url":"/paper/detection-and-localization-of-drosophila-egg","slug":"detection-and-localization-of-drosophila-egg","title":"Detection and Localization of Drosophila Egg Chambers in Microscopy Images.","date":"2017-09-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-dilation-factors-for-semantic","slug":"learning-dilation-factors-for-semantic","title":"Learning Dilation Factors for Semantic Segmentation of Street Scenes","date":"2017-09-06","arxiv_id":"1709.01956","repositories_listed":1,"syntology":null},{"url":"/paper/depthcomp-real-time-depth-image-completion","slug":"depthcomp-real-time-depth-image-completion","title":"DepthComp: real-time depth image completion based on prior semantic scene segmentation","date":"2017-09-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/does-syntax-help-discourse-segmentation-not","slug":"does-syntax-help-discourse-segmentation-not","title":"Does syntax help discourse segmentation? Not so much","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unit-segmentation-of-argumentative-texts","slug":"unit-segmentation-of-argumentative-texts","title":"Unit Segmentation of Argumentative Texts","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-aware-convolutional-networks","slug":"segmentation-aware-convolutional-networks","title":"Segmentation-Aware Convolutional Networks Using Local Attention Masks","date":"2017-08-15","arxiv_id":"1708.04607","repositories_listed":1,"syntology":null},{"url":"/paper/vqs-linking-segmentations-to-questions-and","slug":"vqs-linking-segmentations-to-questions-and","title":"VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation","date":"2017-08-15","arxiv_id":"1708.04686","repositories_listed":1,"syntology":null},{"url":"/paper/fast-scene-understanding-for-autonomous","slug":"fast-scene-understanding-for-autonomous","title":"Fast Scene Understanding for Autonomous Driving","date":"2017-08-08","arxiv_id":"1708.02550","repositories_listed":1,"syntology":null},{"url":"/paper/depth-adaptive-deep-neural-network-for","slug":"depth-adaptive-deep-neural-network-for","title":"Depth Adaptive Deep Neural Network for Semantic Segmentation","date":"2017-08-05","arxiv_id":"1708.01818","repositories_listed":1,"syntology":null},{"url":"/paper/comparison-of-distances-for-supervised","slug":"comparison-of-distances-for-supervised","title":"Comparison of Distances for Supervised Segmentation of White Matter Tractography","date":"2017-08-04","arxiv_id":"1708.01440","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-instance-labeling-leveraging","slug":"semantic-instance-labeling-leveraging","title":"Semantic Instance Labeling Leveraging Hierarchical Segmentation","date":"2017-08-02","arxiv_id":"1708.00946","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-approach-for-image-segmentation-based","slug":"a-novel-approach-for-image-segmentation-based","title":"A Novel Approach for Image Segmentation based on Histograms computed from Hue-data","date":"2017-07-30","arxiv_id":"1707.09643","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-domain-adaptation-for-semantic","slug":"curriculum-domain-adaptation-for-semantic","title":"Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes","date":"2017-07-29","arxiv_id":"1707.09465","repositories_listed":1,"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/curriculum-domain-adaptation-for-semantic#ran","syntology_url":"https://syntology.ai/paper/1707.09465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.09465"}},"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/efficient-yet-deep-convolutional-neural","slug":"efficient-yet-deep-convolutional-neural","title":"Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation","date":"2017-07-26","arxiv_id":"1707.08254","repositories_listed":1,"syntology":null},{"url":"/paper/residual-conv-deconv-grid-network-for","slug":"residual-conv-deconv-grid-network-for","title":"Residual Conv-Deconv Grid Network for Semantic Segmentation","date":"2017-07-25","arxiv_id":"1707.07958","repositories_listed":1,"syntology":null},{"url":"/paper/syllable-aware-neural-language-models-a","slug":"syllable-aware-neural-language-models-a","title":"Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones","date":"2017-07-20","arxiv_id":"1707.06480","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-cardiac-disease-assessment-on-cine","slug":"automatic-cardiac-disease-assessment-on-cine","title":"Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features","date":"2017-07-03","arxiv_id":"1707.00587","repositories_listed":1,"syntology":null},{"url":"/paper/deepigeos-a-deep-interactive-geodesic","slug":"deepigeos-a-deep-interactive-geodesic","title":"DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation","date":"2017-07-03","arxiv_id":"1707.00652","repositories_listed":1,"syntology":null},{"url":"/paper/generalised-wasserstein-dice-score-for","slug":"generalised-wasserstein-dice-score-for","title":"Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks","date":"2017-07-03","arxiv_id":"1707.00478","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-via-structured-patch","slug":"semantic-segmentation-via-structured-patch","title":"Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/image-forgery-localization-based-on-multi","slug":"image-forgery-localization-based-on-multi","title":"Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks","date":"2017-06-13","arxiv_id":"1706.07842","repositories_listed":1,"syntology":null},{"url":"/paper/nighttime-skycloud-image-segmentation","slug":"nighttime-skycloud-image-segmentation","title":"Nighttime sky/cloud image segmentation","date":"2017-05-30","arxiv_id":"1705.10583","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-scene-parsing-with-perspective","slug":"recurrent-scene-parsing-with-perspective","title":"Recurrent Scene Parsing with Perspective Understanding in the Loop","date":"2017-05-20","arxiv_id":"1705.07238","repositories_listed":1,"syntology":null},{"url":"/paper/deep-projective-3d-semantic-segmentation","slug":"deep-projective-3d-semantic-segmentation","title":"Deep Projective 3D Semantic Segmentation","date":"2017-05-09","arxiv_id":"1705.03428","repositories_listed":1,"syntology":null},{"url":"/paper/neural-word-segmentation-with-rich","slug":"neural-word-segmentation-with-rich","title":"Neural Word Segmentation with Rich Pretraining","date":"2017-04-28","arxiv_id":"1704.08960","repositories_listed":1,"syntology":null},{"url":"/paper/unbiased-shape-compactness-for-segmentation","slug":"unbiased-shape-compactness-for-segmentation","title":"Unbiased Shape Compactness for Segmentation","date":"2017-04-28","arxiv_id":"1704.08908","repositories_listed":1,"syntology":null},{"url":"/paper/fast-and-accurate-neural-word-segmentation","slug":"fast-and-accurate-neural-word-segmentation","title":"Fast and Accurate Neural Word Segmentation for Chinese","date":"2017-04-24","arxiv_id":"1704.07047","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-and-cross-domain-discourse-1","slug":"cross-lingual-and-cross-domain-discourse-1","title":"Cross-lingual and cross-domain discourse segmentation of entire documents","date":"2017-04-13","arxiv_id":"1704.04100","repositories_listed":1,"syntology":null},{"url":"/paper/reformulating-level-sets-as-deep-recurrent","slug":"reformulating-level-sets-as-deep-recurrent","title":"Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation","date":"2017-04-12","arxiv_id":"1704.03593","repositories_listed":1,"syntology":null},{"url":"/paper/pixelwise-instance-segmentation-with-a","slug":"pixelwise-instance-segmentation-with-a","title":"Pixelwise Instance Segmentation with a Dynamically Instantiated Network","date":"2017-04-07","arxiv_id":"1704.02386","repositories_listed":1,"syntology":null},{"url":"/paper/character-based-joint-segmentation-and-pos","slug":"character-based-joint-segmentation-and-pos","title":"Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF","date":"2017-04-05","arxiv_id":"1704.01314","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-instance-segmentation-via-deep","slug":"semantic-instance-segmentation-via-deep","title":"Semantic Instance Segmentation via Deep Metric Learning","date":"2017-03-30","arxiv_id":"1703.10277","repositories_listed":1,"syntology":null},{"url":"/paper/labelbank-revisiting-global-perspectives-for","slug":"labelbank-revisiting-global-perspectives-for","title":"LabelBank: Revisiting Global Perspectives for Semantic Segmentation","date":"2017-03-29","arxiv_id":"1703.09891","repositories_listed":1,"syntology":null},{"url":"/paper/segan-segmenting-and-generating-the-invisible","slug":"segan-segmenting-and-generating-the-invisible","title":"SeGAN: Segmenting and Generating the Invisible","date":"2017-03-29","arxiv_id":"1703.10239","repositories_listed":1,"syntology":null},{"url":"/paper/towards-automatic-learning-of-procedures-from","slug":"towards-automatic-learning-of-procedures-from","title":"Towards Automatic Learning of Procedures from Web Instructional Videos","date":"2017-03-28","arxiv_id":"1703.09788","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/towards-automatic-learning-of-procedures-from#ran","syntology_url":"https://syntology.ai/paper/1703.09788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.09788"}},"official":null}},{"url":"/paper/deep-residual-learning-for-instrument","slug":"deep-residual-learning-for-instrument","title":"Deep Residual Learning for Instrument Segmentation in Robotic Surgery","date":"2017-03-24","arxiv_id":"1703.08580","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/deep-residual-learning-for-instrument#ran","syntology_url":"https://syntology.ai/paper/1703.08580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.08580"}},"official":{"repos":["warmspringwinds/tf-image-segmentation"],"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/recurrent-multimodal-interaction-for","slug":"recurrent-multimodal-interaction-for","title":"Recurrent Multimodal Interaction for Referring Image Segmentation","date":"2017-03-23","arxiv_id":"1703.07939","repositories_listed":1,"syntology":null},{"url":"/paper/neural-ctrl-f-segmentation-free-query-by","slug":"neural-ctrl-f-segmentation-free-query-by","title":"Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections","date":"2017-03-22","arxiv_id":"1703.07645","repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-for-semantic-segmentation-of","slug":"algorithms-for-semantic-segmentation-of","title":"Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning","date":"2017-03-19","arxiv_id":"1703.06452","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-deep-learning-for-fully","slug":"semi-supervised-deep-learning-for-fully","title":"Semi-Supervised Deep Learning for Fully Convolutional Networks","date":"2017-03-17","arxiv_id":"1703.06000","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-skin-lesion-segmentation-with-fully","slug":"automatic-skin-lesion-segmentation-with-fully","title":"Automatic skin lesion segmentation with fully convolutional-deconvolutional networks","date":"2017-03-15","arxiv_id":"1703.05165","repositories_listed":1,"syntology":null},{"url":"/paper/deep-value-networks-learn-to-evaluate-and","slug":"deep-value-networks-learn-to-evaluate-and","title":"Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs","date":"2017-03-13","arxiv_id":"1703.04363","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-saliency-map-driven-segmentation","slug":"supervised-saliency-map-driven-segmentation","title":"Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images","date":"2017-02-28","arxiv_id":"1703.00087","repositories_listed":1,"syntology":null},{"url":"/paper/pixelnet-representation-of-the-pixels-by-the","slug":"pixelnet-representation-of-the-pixels-by-the","title":"PixelNet: Representation of the pixels, by the pixels, and for the pixels","date":"2017-02-21","arxiv_id":"1702.06506","repositories_listed":1,"syntology":null},{"url":"/paper/systematic-study-of-color-spaces-and","slug":"systematic-study-of-color-spaces-and","title":"Systematic study of color spaces and components for the segmentation of sky/cloud images","date":"2017-01-17","arxiv_id":"1701.04520","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-approach-to-vietnamese-word","slug":"a-hybrid-approach-to-vietnamese-word","title":"A hybrid approach to Vietnamese word segmentation","date":"2016-12-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/superpixel-segmentation-using-gaussian","slug":"superpixel-segmentation-using-gaussian","title":"Superpixel Segmentation Using Gaussian Mixture Model","date":"2016-12-28","arxiv_id":"1612.08792","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-domain-adaptation-in-brain","slug":"unsupervised-domain-adaptation-in-brain","title":"Unsupervised domain adaptation in brain lesion segmentation with adversarial networks","date":"2016-12-28","arxiv_id":"1612.08894","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-deep-structural-networks-for","slug":"adversarial-deep-structural-networks-for","title":"Adversarial Deep Structural Networks for Mammographic Mass Segmentation","date":"2016-12-18","arxiv_id":"1612.05970","repositories_listed":1,"syntology":null},{"url":"/paper/3d-shape-segmentation-with-projective","slug":"3d-shape-segmentation-with-projective","title":"3D Shape Segmentation with Projective Convolutional Networks","date":"2016-12-08","arxiv_id":"1612.02808","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-ground-level-scene-layout-from","slug":"predicting-ground-level-scene-layout-from","title":"Predicting Ground-Level Scene Layout from Aerial Imagery","date":"2016-12-08","arxiv_id":"1612.02709","repositories_listed":1,"syntology":null},{"url":"/paper/classification-with-an-edge-improving","slug":"classification-with-an-edge-improving","title":"Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection","date":"2016-12-05","arxiv_id":"1612.01337","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-using-adversarial","slug":"semantic-segmentation-using-adversarial","title":"Semantic Segmentation using Adversarial Networks","date":"2016-11-25","arxiv_id":"1611.08408","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-important-objects","slug":"unsupervised-learning-of-important-objects","title":"Unsupervised Learning of Important Objects from First-Person Videos","date":"2016-11-16","arxiv_id":"1611.05335","repositories_listed":1,"syntology":null},{"url":"/paper/scale-constrained-unsupervised-evaluation","slug":"scale-constrained-unsupervised-evaluation","title":"Scale-constrained Unsupervised Evaluation Method for Multi-scale Image Segmentation","date":"2016-11-15","arxiv_id":"1611.04850","repositories_listed":1,"syntology":null},{"url":"/paper/rough-set-based-color-channel-selection","slug":"rough-set-based-color-channel-selection","title":"Rough Set Based Color Channel Selection","date":"2016-11-03","arxiv_id":"1611.00931","repositories_listed":1,"syntology":null},{"url":"/paper/stuffnet-using-stuff-to-improve-object","slug":"stuffnet-using-stuff-to-improve-object","title":"StuffNet: Using 'Stuff' to Improve Object Detection","date":"2016-10-19","arxiv_id":"1610.05861","repositories_listed":1,"syntology":null},{"url":"/paper/deep-retinal-image-understanding","slug":"deep-retinal-image-understanding","title":"Deep Retinal Image Understanding","date":"2016-09-05","arxiv_id":"1609.01103","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-convolutional-networks-a-unified","slug":"temporal-convolutional-networks-a-unified","title":"Temporal Convolutional Networks: A Unified Approach to Action Segmentation","date":"2016-08-29","arxiv_id":"1608.08242","repositories_listed":1,"syntology":null},{"url":"/paper/stfcn-spatio-temporal-fcn-for-semantic-video","slug":"stfcn-spatio-temporal-fcn-for-semantic-video","title":"STFCN: Spatio-Temporal FCN for Semantic Video Segmentation","date":"2016-08-21","arxiv_id":"1608.05971","repositories_listed":1,"syntology":null},{"url":"/paper/sshmt-semi-supervised-hierarchical-merge-tree","slug":"sshmt-semi-supervised-hierarchical-merge-tree","title":"SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation","date":"2016-08-14","arxiv_id":"1608.04051","repositories_listed":1,"syntology":null},{"url":"/paper/clockwork-convnets-for-video-semantic","slug":"clockwork-convnets-for-video-semantic","title":"Clockwork Convnets for Video Semantic Segmentation","date":"2016-08-11","arxiv_id":"1608.03609","repositories_listed":1,"syntology":null}],"record_sha256":"91af19e1fd124dd287dbca76f779680ea0b57f54cb74a922ae27840e95719fbb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}