{"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/35","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":35,"pages_in_order":131,"rows_per_page":100,"rows":[3401,3500],"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/34","next":"/task/segmentation/papers/36","papers":[{"url":"/paper/bioculargan-bimodal-synthesis-and-annotation","slug":"bioculargan-bimodal-synthesis-and-annotation","title":"BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images","date":"2022-05-03","arxiv_id":"2205.01536","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-framework-for-real-time-fetal","slug":"deep-learning-framework-for-real-time-fetal","title":"Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI","date":"2022-05-02","arxiv_id":"2205.01675","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-address-segmentation-for","slug":"fine-grained-address-segmentation-for","title":"Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching","date":"2022-05-01","arxiv_id":"2205.02269","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-chinese-word-segmentation-with-1","slug":"unsupervised-chinese-word-segmentation-with-1","title":"Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-word-segmentation-for","slug":"weakly-supervised-word-segmentation-for","title":"Weakly Supervised Word Segmentation for Computational Language Documentation","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weighted-self-distillation-for-chinese-word","slug":"weighted-self-distillation-for-chinese-word","title":"Weighted self Distillation for Chinese word segmentation","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-segmentation-by-separation-inference-for","slug":"word-segmentation-by-separation-inference-for","title":"Word Segmentation by Separation Inference for East Asian Languages","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/elucidating-meta-structures-of-noisy-labels","slug":"elucidating-meta-structures-of-noisy-labels","title":"Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks","date":"2022-04-30","arxiv_id":"2205.00160","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-musical-form-recognition","slug":"deep-learning-for-musical-form-recognition","title":"Deep Learning for Musical Form: Recognition and Analysis","date":"2022-04-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/computer-vision-for-road-imaging-and-pothole","slug":"computer-vision-for-road-imaging-and-pothole","title":"Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms","date":"2022-04-28","arxiv_id":"2204.13590","repositories_listed":1,"syntology":null},{"url":"/paper/hrda-context-aware-high-resolution-domain","slug":"hrda-context-aware-high-resolution-domain","title":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation","date":"2022-04-27","arxiv_id":"2204.13132","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-object-parts-for","slug":"self-supervised-learning-of-object-parts-for","title":"Self-Supervised Learning of Object Parts for Semantic Segmentation","date":"2022-04-27","arxiv_id":"2204.13101","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"6 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-supervised-learning-of-object-parts-for#ran","syntology_url":"https://syntology.ai/paper/2204.13101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.13101"}},"official":{"repos":["mkuuwaujinga/leopart"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-segmentation-of-hyperspectral-1","slug":"unsupervised-segmentation-of-hyperspectral-1","title":"Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels","date":"2022-04-26","arxiv_id":"2204.12296","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-hierarchical-semantic","slug":"unsupervised-hierarchical-semantic","title":"Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers","date":"2022-04-25","arxiv_id":"2204.11432","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":2,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-hierarchical-semantic#ran","syntology_url":"https://syntology.ai/paper/2204.11432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.11432"}},"official":{"repos":["twke18/hsg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/embedtrack-simultaneous-cell-segmentation-and","slug":"embedtrack-simultaneous-cell-segmentation-and","title":"EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths","date":"2022-04-22","arxiv_id":"2204.10713","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-the-prototype-divide-and-conquer","slug":"beyond-the-prototype-divide-and-conquer","title":"Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation","date":"2022-04-21","arxiv_id":"2204.09903","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":4,"n_ran_checked":4,"n_instrument":8,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"12 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 8 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/beyond-the-prototype-divide-and-conquer#ran","syntology_url":"https://syntology.ai/paper/2204.09903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09903"}},"official":{"repos":["chunbolang/DCP"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/color-invariant-skin-segmentation","slug":"color-invariant-skin-segmentation","title":"Color Invariant Skin Segmentation","date":"2022-04-21","arxiv_id":"2204.09882","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-segmentation-and-visualization","slug":"interactive-segmentation-and-visualization","title":"Interactive Segmentation and Visualization for Tiny Objects in Multi-megapixel Images","date":"2022-04-21","arxiv_id":"2204.10356","repositories_listed":1,"syntology":{"n":20,"n_ran":19,"n_constructed":7,"n_ran_checked":9,"n_instrument":10,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":20,"phrase":"19 ran (of which 7 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 10 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/interactive-segmentation-and-visualization#ran","syntology_url":"https://syntology.ai/paper/2204.10356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.10356"}},"official":{"repos":["cy-xu/cosmic-conn"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":7,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-domain-adaptation-for-cardiac","slug":"unsupervised-domain-adaptation-for-cardiac","title":"Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization","date":"2022-04-20","arxiv_id":"2204.09334","repositories_listed":1,"syntology":null},{"url":"/paper/a-fully-automatic-ai-system-for-tooth-and","slug":"a-fully-automatic-ai-system-for-tooth-and","title":"A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images","date":"2022-04-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dual-domain-image-synthesis-using","slug":"dual-domain-image-synthesis-using","title":"Dual-Domain Image Synthesis using Segmentation-Guided GAN","date":"2022-04-19","arxiv_id":"2204.09015","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-3d-shape-segmentation-with","slug":"semi-supervised-3d-shape-segmentation-with","title":"Semi-supervised 3D shape segmentation with multilevel consistency and part substitution","date":"2022-04-19","arxiv_id":"2204.08824","repositories_listed":1,"syntology":null},{"url":"/paper/two-stream-graph-convolutional-network-for","slug":"two-stream-graph-convolutional-network-for","title":"Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation","date":"2022-04-19","arxiv_id":"2204.08797","repositories_listed":1,"syntology":null},{"url":"/paper/continual-hippocampus-segmentation-with","slug":"continual-hippocampus-segmentation-with","title":"Continual Hippocampus Segmentation with Transformers","date":"2022-04-17","arxiv_id":"2204.08043","repositories_listed":1,"syntology":null},{"url":"/paper/language-grounded-indoor-3d-semantic","slug":"language-grounded-indoor-3d-semantic","title":"Language-Grounded Indoor 3D Semantic Segmentation in the Wild","date":"2022-04-16","arxiv_id":"2204.07761","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/language-grounded-indoor-3d-semantic#ran","syntology_url":"https://syntology.ai/paper/2204.07761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07761"}},"official":{"repos":["RozDavid/LanguageGroundedSemseg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/u-net-a-spatiospectral-network-for-retinal","slug":"u-net-a-spatiospectral-network-for-retinal","title":"Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation","date":"2022-04-15","arxiv_id":"2204.07613","repositories_listed":1,"syntology":null},{"url":"/paper/cross-image-relational-knowledge-distillation","slug":"cross-image-relational-knowledge-distillation","title":"Cross-Image Relational Knowledge Distillation for Semantic Segmentation","date":"2022-04-14","arxiv_id":"2204.06986","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-image-relational-knowledge-distillation#ran","syntology_url":"https://syntology.ai/paper/2204.06986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06986"}},"official":{"repos":["winycg/cirkd"],"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/interactive-object-segmentation-in-3d-point","slug":"interactive-object-segmentation-in-3d-point","title":"Interactive Object Segmentation in 3D Point Clouds","date":"2022-04-14","arxiv_id":"2204.07183","repositories_listed":1,"syntology":null},{"url":"/paper/joint-forecasting-of-panoptic-segmentations","slug":"joint-forecasting-of-panoptic-segmentations","title":"Joint Forecasting of Panoptic Segmentations with Difference Attention","date":"2022-04-14","arxiv_id":"2204.07157","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/joint-forecasting-of-panoptic-segmentations#ran","syntology_url":"https://syntology.ai/paper/2204.07157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07157"}},"official":{"repos":["cgraber/psf-diffattn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lefm-nets-learnable-explicit-feature-map-deep","slug":"lefm-nets-learnable-explicit-feature-map-deep","title":"LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections","date":"2022-04-14","arxiv_id":"2204.06955","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-deep-learning-meets-chan-vese","slug":"unsupervised-deep-learning-meets-chan-vese","title":"Unsupervised Deep Learning Meets Chan-Vese Model","date":"2022-04-14","arxiv_id":"2204.06951","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-memory-management-for-video-object","slug":"adaptive-memory-management-for-video-object","title":"Adaptive Memory Management for Video Object Segmentation","date":"2022-04-13","arxiv_id":"2204.06626","repositories_listed":1,"syntology":null},{"url":"/paper/glass-segmentation-with-rgb-thermal-image","slug":"glass-segmentation-with-rgb-thermal-image","title":"Glass Segmentation with RGB-Thermal Image Pairs","date":"2022-04-12","arxiv_id":"2204.05453","repositories_listed":1,"syntology":null},{"url":"/paper/nightlab-a-dual-level-architecture-with","slug":"nightlab-a-dual-level-architecture-with","title":"NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night","date":"2022-04-12","arxiv_id":"2204.05538","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-hierarchies-by-their-consistent","slug":"assessing-hierarchies-by-their-consistent","title":"Assessing hierarchies by their consistent segmentations","date":"2022-04-11","arxiv_id":"2204.04969","repositories_listed":1,"syntology":null},{"url":"/paper/hft-lifting-perspective-representations-via","slug":"hft-lifting-perspective-representations-via","title":"HFT: Lifting Perspective Representations via Hybrid Feature Transformation","date":"2022-04-11","arxiv_id":"2204.05068","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-consistent-probabilistic-lesion","slug":"segmentation-consistent-probabilistic-lesion","title":"Segmentation-Consistent Probabilistic Lesion Counting","date":"2022-04-11","arxiv_id":"2204.05276","repositories_listed":1,"syntology":null},{"url":"/paper/consinstancy-learning-instance","slug":"consinstancy-learning-instance","title":"ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles","date":"2022-04-10","arxiv_id":"2204.04635","repositories_listed":1,"syntology":null},{"url":"/paper/fashionformer-a-simple-effective-and-unified","slug":"fashionformer-a-simple-effective-and-unified","title":"Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition","date":"2022-04-10","arxiv_id":"2204.04654","repositories_listed":1,"syntology":null},{"url":"/paper/panoptic-partformer-learning-a-unified-model","slug":"panoptic-partformer-learning-a-unified-model","title":"Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation","date":"2022-04-10","arxiv_id":"2204.04655","repositories_listed":1,"syntology":null},{"url":"/paper/video-k-net-a-simple-strong-and-unified","slug":"video-k-net-a-simple-strong-and-unified","title":"Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation","date":"2022-04-10","arxiv_id":"2204.04656","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-to-fine-feature-mining-for-video","slug":"coarse-to-fine-feature-mining-for-video","title":"Learning Local and Global Temporal Contexts for Video Semantic Segmentation","date":"2022-04-07","arxiv_id":"2204.03330","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-multiscale-object-based-superpixel","slug":"efficient-multiscale-object-based-superpixel","title":"Efficient Multiscale Object-based Superpixel Framework","date":"2022-04-07","arxiv_id":"2204.03533","repositories_listed":1,"syntology":null},{"url":"/paper/mc-unet-multi-module-concatenation-based-on-u","slug":"mc-unet-multi-module-concatenation-based-on-u","title":"MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation","date":"2022-04-07","arxiv_id":"2204.03213","repositories_listed":1,"syntology":null},{"url":"/paper/pin-the-memory-learning-to-generalize","slug":"pin-the-memory-learning-to-generalize","title":"Pin the Memory: Learning to Generalize Semantic Segmentation","date":"2022-04-07","arxiv_id":"2204.03609","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/pin-the-memory-learning-to-generalize#ran","syntology_url":"https://syntology.ai/paper/2204.03609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03609"}},"official":{"repos":["genie-kim/pinthememory"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/task-aware-active-learning-for-endoscopic","slug":"task-aware-active-learning-for-endoscopic","title":"Task-Aware Active Learning for Endoscopic Image Analysis","date":"2022-04-07","arxiv_id":"2204.03440","repositories_listed":1,"syntology":null},{"url":"/paper/efficientcellseg-efficient-volumetric-cell","slug":"efficientcellseg-efficient-volumetric-cell","title":"EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring","date":"2022-04-06","arxiv_id":"2204.03014","repositories_listed":1,"syntology":null},{"url":"/paper/focalclick-towards-practical-interactive","slug":"focalclick-towards-practical-interactive","title":"FocalClick: Towards Practical Interactive Image Segmentation","date":"2022-04-06","arxiv_id":"2204.02574","repositories_listed":1,"syntology":{"n":21,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"14 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; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/focalclick-towards-practical-interactive#ran","syntology_url":"https://syntology.ai/paper/2204.02574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02574"}},"official":{"repos":["XavierCHEN34/ClickSEG"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-motion-with-multi-modal-features-for","slug":"modeling-motion-with-multi-modal-features-for","title":"Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation","date":"2022-04-06","arxiv_id":"2204.02547","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-semantic-segmentation-with-5","slug":"semi-supervised-semantic-segmentation-with-5","title":"Semi-supervised Semantic Segmentation with Error Localization Network","date":"2022-04-05","arxiv_id":"2204.02078","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/semi-supervised-semantic-segmentation-with-5#ran","syntology_url":"https://syntology.ai/paper/2204.02078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02078"}},"official":{"repos":["kinux98/SSL_ELN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/doda-data-oriented-sim-to-real-domain","slug":"doda-data-oriented-sim-to-real-domain","title":"DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation","date":"2022-04-04","arxiv_id":"2204.01599","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/doda-data-oriented-sim-to-real-domain#ran","syntology_url":"https://syntology.ai/paper/2204.01599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01599"}},"official":{"repos":["cvmi-lab/doda"],"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/spfnet-subspace-pyramid-fusion-network-for","slug":"spfnet-subspace-pyramid-fusion-network-for","title":"Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation","date":"2022-04-04","arxiv_id":"2204.01278","repositories_listed":1,"syntology":null},{"url":"/paper/histogram-of-oriented-gradients-meet-deep","slug":"histogram-of-oriented-gradients-meet-deep","title":"Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation","date":"2022-04-02","arxiv_id":"2204.01712","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-domain-generalized","slug":"semantic-aware-domain-generalized","title":"Semantic-Aware Domain Generalized Segmentation","date":"2022-04-02","arxiv_id":"2204.00822","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/semantic-aware-domain-generalized#ran","syntology_url":"https://syntology.ai/paper/2204.00822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00822"}},"official":{"repos":["leolyj/san-saw"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/frnet-factorized-and-regular-blocks-network","slug":"frnet-factorized-and-regular-blocks-network","title":"FRNet: Factorized and Regular Blocks Network for Semantic Segmentation in Road Scene","date":"2022-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/online-panoptic-3d-reconstruction-as-a-linear","slug":"online-panoptic-3d-reconstruction-as-a-linear","title":"Online panoptic 3D reconstruction as a Linear Assignment Problem","date":"2022-04-01","arxiv_id":"2204.00231","repositories_listed":1,"syntology":null},{"url":"/paper/unetformer-a-unified-vision-transformer-model","slug":"unetformer-a-unified-vision-transformer-model","title":"UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation","date":"2022-04-01","arxiv_id":"2204.00631","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unetformer-a-unified-vision-transformer-model#ran","syntology_url":"https://syntology.ai/paper/2204.00631","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00631"}},"official":{"repos":["project-monai/research-contributions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tooth-instance-segmentation-on-panoramic","slug":"tooth-instance-segmentation-on-panoramic","title":"Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing","date":"2022-03-31","arxiv_id":"2204.00095","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-road-to-online-adaptation-for-semantic","slug":"on-the-road-to-online-adaptation-for-semantic","title":"On the Road to Online Adaptation for Semantic Image Segmentation","date":"2022-03-30","arxiv_id":"2203.16195","repositories_listed":1,"syntology":null},{"url":"/paper/cat-net-a-cross-slice-attention-transformer","slug":"cat-net-a-cross-slice-attention-transformer","title":"CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI","date":"2022-03-29","arxiv_id":"2203.15163","repositories_listed":1,"syntology":null},{"url":"/paper/category-guided-attention-network-for-brain","slug":"category-guided-attention-network-for-brain","title":"Category Guided Attention Network for Brain Tumor Segmentation in MRI","date":"2022-03-29","arxiv_id":"2203.15383","repositories_listed":1,"syntology":null},{"url":"/paper/integrative-few-shot-learning-for","slug":"integrative-few-shot-learning-for","title":"Integrative Few-Shot Learning for Classification and Segmentation","date":"2022-03-29","arxiv_id":"2203.15712","repositories_listed":1,"syntology":null},{"url":"/paper/mc-beit-multi-choice-discretization-for-image","slug":"mc-beit-multi-choice-discretization-for-image","title":"mc-BEiT: Multi-choice Discretization for Image BERT Pre-training","date":"2022-03-29","arxiv_id":"2203.15371","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mc-beit-multi-choice-discretization-for-image#ran","syntology_url":"https://syntology.ai/paper/2203.15371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15371"}},"official":{"repos":["lixiaotong97/mc-beit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/min-max-similarity-a-contrastive-learning","slug":"min-max-similarity-a-contrastive-learning","title":"Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation","date":"2022-03-29","arxiv_id":"2203.15177","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-leaf-segmentation-under","slug":"self-supervised-leaf-segmentation-under","title":"Self-Supervised Leaf Segmentation under Complex Lighting Conditions","date":"2022-03-29","arxiv_id":"2203.15943","repositories_listed":1,"syntology":null},{"url":"/paper/simt-handling-open-set-noise-for-domain","slug":"simt-handling-open-set-noise-for-domain","title":"SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation","date":"2022-03-29","arxiv_id":"2203.15202","repositories_listed":1,"syntology":null},{"url":"/paper/speech-segmentation-optimization-using","slug":"speech-segmentation-optimization-using","title":"Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation","date":"2022-03-29","arxiv_id":"2203.15479","repositories_listed":1,"syntology":null},{"url":"/paper/a-distribution-dependent-mumford-shah-model","slug":"a-distribution-dependent-mumford-shah-model","title":"A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation","date":"2022-03-28","arxiv_id":"2203.15058","repositories_listed":1,"syntology":null},{"url":"/paper/deep-interactive-learning-based-ovarian","slug":"deep-interactive-learning-based-ovarian","title":"Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation","date":"2022-03-28","arxiv_id":"2203.15015","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-semantic-segmentation-a-prototype","slug":"rethinking-semantic-segmentation-a-prototype","title":"Rethinking Semantic Segmentation: A Prototype View","date":"2022-03-28","arxiv_id":"2203.15102","repositories_listed":1,"syntology":null},{"url":"/paper/specialized-document-embeddings-for-aspect","slug":"specialized-document-embeddings-for-aspect","title":"Specialized Document Embeddings for Aspect-based Similarity of Research Papers","date":"2022-03-28","arxiv_id":"2203.14541","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-by-early-region-proxy","slug":"semantic-segmentation-by-early-region-proxy","title":"Semantic Segmentation by Early Region Proxy","date":"2022-03-26","arxiv_id":"2203.14043","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/semantic-segmentation-by-early-region-proxy#ran","syntology_url":"https://syntology.ai/paper/2203.14043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14043"}},"official":{"repos":["yif-zhang/regionproxy"],"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/neural-networks-with-divisive-normalization","slug":"neural-networks-with-divisive-normalization","title":"Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset","date":"2022-03-25","arxiv_id":"2203.13558","repositories_listed":1,"syntology":null},{"url":"/paper/noisy-boundaries-lemon-or-lemonade-for-semi","slug":"noisy-boundaries-lemon-or-lemonade-for-semi","title":"Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?","date":"2022-03-25","arxiv_id":"2203.13427","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-few-shot-semantic-segmentation","slug":"cross-domain-few-shot-semantic-segmentation","title":"Cross-Domain Few-Shot Semantic Segmentation","date":"2022-03-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hmfs-hybrid-masking-for-few-shot-segmentation","slug":"hmfs-hybrid-masking-for-few-shot-segmentation","title":"HM: Hybrid Masking for Few-Shot Segmentation","date":"2022-03-24","arxiv_id":"2203.12826","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/hmfs-hybrid-masking-for-few-shot-segmentation#ran","syntology_url":"https://syntology.ai/paper/2203.12826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12826"}},"official":{"repos":["moonsh/hm-hybrid-masking"],"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/dan-a-segmentation-free-document-attention","slug":"dan-a-segmentation-free-document-attention","title":"DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition","date":"2022-03-23","arxiv_id":"2203.12273","repositories_listed":1,"syntology":null},{"url":"/paper/mt-uda-towards-unsupervised-cross-modality","slug":"mt-uda-towards-unsupervised-cross-modality","title":"MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels","date":"2022-03-23","arxiv_id":"2203.12454","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-hybrid-spine-network-for","slug":"semi-supervised-hybrid-spine-network-for","title":"Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images","date":"2022-03-23","arxiv_id":"2203.12151","repositories_listed":1,"syntology":null},{"url":"/paper/cp2-copy-paste-contrastive-pretraining-for","slug":"cp2-copy-paste-contrastive-pretraining-for","title":"CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation","date":"2022-03-22","arxiv_id":"2203.11709","repositories_listed":1,"syntology":null},{"url":"/paper/learning-patch-to-cluster-attention-in-vision","slug":"learning-patch-to-cluster-attention-in-vision","title":"PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers","date":"2022-03-22","arxiv_id":"2203.11987","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":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/learning-patch-to-cluster-attention-in-vision#ran","syntology_url":"https://syntology.ai/paper/2203.11987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11987"}},"official":{"repos":["ivmcl/pacavit"],"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/drive-segment-unsupervised-semantic","slug":"drive-segment-unsupervised-semantic","title":"Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation","date":"2022-03-21","arxiv_id":"2203.11160","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/drive-segment-unsupervised-semantic#ran","syntology_url":"https://syntology.ai/paper/2203.11160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11160"}},"official":{"repos":["vobecant/DriveAndSegment"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tree-energy-loss-towards-sparsely-annotated","slug":"tree-energy-loss-towards-sparsely-annotated","title":"Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation","date":"2022-03-21","arxiv_id":"2203.10739","repositories_listed":1,"syntology":null},{"url":"/paper/learning-morphological-feature-perturbations","slug":"learning-morphological-feature-perturbations","title":"Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation","date":"2022-03-19","arxiv_id":"2203.10196","repositories_listed":1,"syntology":null},{"url":"/paper/learning-self-supervised-low-rank-network-for","slug":"learning-self-supervised-low-rank-network-for","title":"Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation","date":"2022-03-19","arxiv_id":"2203.10278","repositories_listed":1,"syntology":null},{"url":"/paper/towards-robust-semantic-segmentation-of","slug":"towards-robust-semantic-segmentation-of","title":"Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning","date":"2022-03-19","arxiv_id":"2203.10395","repositories_listed":1,"syntology":null},{"url":"/paper/class-balanced-pixel-level-self-labeling-for","slug":"class-balanced-pixel-level-self-labeling-for","title":"Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation","date":"2022-03-18","arxiv_id":"2203.09744","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/class-balanced-pixel-level-self-labeling-for#ran","syntology_url":"https://syntology.ai/paper/2203.09744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09744"}},"official":{"repos":["lslrh/cpsl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastmask-contrastive-learning-to-segment","slug":"contrastmask-contrastive-learning-to-segment","title":"ContrastMask: Contrastive Learning to Segment Every Thing","date":"2022-03-18","arxiv_id":"2203.09775","repositories_listed":1,"syntology":null},{"url":"/paper/ess-learning-event-based-semantic","slug":"ess-learning-event-based-semantic","title":"ESS: Learning Event-based Semantic Segmentation from Still Images","date":"2022-03-18","arxiv_id":"2203.10016","repositories_listed":1,"syntology":null},{"url":"/paper/local-global-context-aware-transformer-for","slug":"local-global-context-aware-transformer-for","title":"Local-Global Context Aware Transformer for Language-Guided Video Segmentation","date":"2022-03-18","arxiv_id":"2203.09773","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":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/local-global-context-aware-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.09773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09773"}},"official":{"repos":["leonnnop/locater"],"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/unbiased-subclass-regularization-for-semi","slug":"unbiased-subclass-regularization-for-semi","title":"Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation","date":"2022-03-18","arxiv_id":"2203.10026","repositories_listed":1,"syntology":null},{"url":"/paper/cyborgs-contrastively-bootstrapping-object","slug":"cyborgs-contrastively-bootstrapping-object","title":"CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation","date":"2022-03-17","arxiv_id":"2203.09343","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/cyborgs-contrastively-bootstrapping-object#ran","syntology_url":"https://syntology.ai/paper/2203.09343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09343"}},"official":{"repos":["renwang435/cyborgs"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/label-conditioned-segmentation","slug":"label-conditioned-segmentation","title":"Label conditioned segmentation","date":"2022-03-17","arxiv_id":"2203.10091","repositories_listed":1,"syntology":null},{"url":"/paper/multi-similarity-based-hyperrelation-network","slug":"multi-similarity-based-hyperrelation-network","title":"Multi-similarity based Hyperrelation Network for few-shot segmentation","date":"2022-03-17","arxiv_id":"2203.09550","repositories_listed":1,"syntology":null},{"url":"/paper/data-efficient-3d-learner-via-knowledge","slug":"data-efficient-3d-learner-via-knowledge","title":"Data Efficient 3D Learner via Knowledge Transferred from 2D Model","date":"2022-03-16","arxiv_id":"2203.08479","repositories_listed":1,"syntology":null},{"url":"/paper/graph-flow-cross-layer-graph-flow","slug":"graph-flow-cross-layer-graph-flow","title":"Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation","date":"2022-03-16","arxiv_id":"2203.08667","repositories_listed":1,"syntology":null},{"url":"/paper/object-discovery-and-representation-networks","slug":"object-discovery-and-representation-networks","title":"Object discovery and representation networks","date":"2022-03-16","arxiv_id":"2203.08777","repositories_listed":1,"syntology":null},{"url":"/paper/point-unet-a-context-aware-point-based-neural","slug":"point-unet-a-context-aware-point-based-neural","title":"Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation","date":"2022-03-16","arxiv_id":"2203.08964","repositories_listed":1,"syntology":null},{"url":"/paper/carts-causality-driven-robot-tool","slug":"carts-causality-driven-robot-tool","title":"CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data","date":"2022-03-15","arxiv_id":"2203.09475","repositories_listed":1,"syntology":null},{"url":"/paper/sats-self-attention-transfer-for-continual","slug":"sats-self-attention-transfer-for-continual","title":"SATS: Self-Attention Transfer for Continual Semantic Segmentation","date":"2022-03-15","arxiv_id":"2203.07667","repositories_listed":1,"syntology":null}],"record_sha256":"fd6dc00c6c47209d8592ccb89b7b0482f09d655b1e3c459824c9afdb05f9f714","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}