{"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/20","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":20,"pages_in_order":131,"rows_per_page":100,"rows":[1901,2000],"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/19","next":"/task/segmentation/papers/21","papers":[{"url":"/paper/mask-enhanced-segment-anything-model-for","slug":"mask-enhanced-segment-anything-model-for","title":"Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation","date":"2024-03-09","arxiv_id":"2403.05912","repositories_listed":1,"syntology":null},{"url":"/paper/cracknex-a-few-shot-low-light-crack","slug":"cracknex-a-few-shot-low-light-crack","title":"CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections","date":"2024-03-05","arxiv_id":"2403.03063","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-weakly-supervised-3d-medical-image","slug":"enhancing-weakly-supervised-3d-medical-image","title":"Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning","date":"2024-03-05","arxiv_id":"2403.02566","repositories_listed":1,"syntology":null},{"url":"/paper/learning-zero-shot-material-states","slug":"learning-zero-shot-material-states","title":"Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data","date":"2024-03-05","arxiv_id":"2403.03309","repositories_listed":1,"syntology":null},{"url":"/paper/eagle-eigen-aggregation-learning-for-object","slug":"eagle-eigen-aggregation-learning-for-object","title":"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation","date":"2024-03-03","arxiv_id":"2403.01482","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/eagle-eigen-aggregation-learning-for-object#ran","syntology_url":"https://syntology.ai/paper/2403.01482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01482"}},"official":{"repos":["MICV-yonsei/EAGLE"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-retinal-vascular-structure","slug":"enhancing-retinal-vascular-structure","title":"Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model","date":"2024-03-03","arxiv_id":"2403.01362","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-segmentation-models-with-mask","slug":"benchmarking-segmentation-models-with-mask","title":"Benchmarking Segmentation Models with Mask-Preserved Attribute Editing","date":"2024-03-02","arxiv_id":"2403.01231","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-few-shot-3d-point-cloud-semantic","slug":"rethinking-few-shot-3d-point-cloud-semantic","title":"Rethinking Few-shot 3D Point Cloud Semantic Segmentation","date":"2024-03-01","arxiv_id":"2403.00592","repositories_listed":1,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":16,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/rethinking-few-shot-3d-point-cloud-semantic#ran","syntology_url":"https://syntology.ai/paper/2403.00592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00592"}},"official":{"repos":["zhaochongan/coseg"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/fusionvision-a-comprehensive-approach-of-3d","slug":"fusionvision-a-comprehensive-approach-of-3d","title":"FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything","date":"2024-02-29","arxiv_id":"2403.00175","repositories_listed":1,"syntology":null},{"url":"/paper/pem-prototype-based-efficient-maskformer-for","slug":"pem-prototype-based-efficient-maskformer-for","title":"PEM: Prototype-based Efficient MaskFormer for Image Segmentation","date":"2024-02-29","arxiv_id":"2402.19422","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pem-prototype-based-efficient-maskformer-for#ran","syntology_url":"https://syntology.ai/paper/2402.19422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19422"}},"official":{"repos":["niccolocavagnero/pem"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-generalized-segmentation-for-foggy","slug":"learning-generalized-segmentation-for-foggy","title":"Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance","date":"2024-02-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/spannotation-enhancing-semantic-segmentation","slug":"spannotation-enhancing-semantic-segmentation","title":"Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation with Efficient Image Annotation","date":"2024-02-28","arxiv_id":"2402.18084","repositories_listed":1,"syntology":null},{"url":"/paper/adapt-before-comparison-a-new-perspective-on","slug":"adapt-before-comparison-a-new-perspective-on","title":"Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation","date":"2024-02-27","arxiv_id":"2402.17614","repositories_listed":1,"syntology":{"n":23,"n_ran":17,"n_constructed":0,"n_ran_checked":10,"n_instrument":7,"n_unverified":6,"n_honours":2,"n_violates":0,"n_no_contract":8,"n_pointer_only":23,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 7 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/adapt-before-comparison-a-new-perspective-on#ran","syntology_url":"https://syntology.ai/paper/2402.17614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17614"}},"official":{"repos":["vision-kek/abcdfss"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/fedlppa-learning-personalized-prompt-and","slug":"fedlppa-learning-personalized-prompt-and","title":"FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation","date":"2024-02-27","arxiv_id":"2402.17502","repositories_listed":1,"syntology":null},{"url":"/paper/medcontext-learning-contextual-cues-for","slug":"medcontext-learning-contextual-cues-for","title":"MedContext: Learning Contextual Cues for Efficient Volumetric Medical Segmentation","date":"2024-02-27","arxiv_id":"2402.17725","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-hides-class-promoting-scribble-based","slug":"scribble-hides-class-promoting-scribble-based","title":"Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label","date":"2024-02-27","arxiv_id":"2402.17555","repositories_listed":1,"syntology":null},{"url":"/paper/segment-anything-model-for-head-and-neck","slug":"segment-anything-model-for-head-and-neck","title":"Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images","date":"2024-02-27","arxiv_id":"2402.17454","repositories_listed":1,"syntology":null},{"url":"/paper/vrp-sam-sam-with-visual-reference-prompt","slug":"vrp-sam-sam-with-visual-reference-prompt","title":"VRP-SAM: SAM with Visual Reference Prompt","date":"2024-02-27","arxiv_id":"2402.17726","repositories_listed":1,"syntology":{"n":38,"n_ran":25,"n_constructed":4,"n_ran_checked":11,"n_instrument":14,"n_unverified":13,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":7,"phrase":"25 ran (of which 4 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 14 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/vrp-sam-sam-with-visual-reference-prompt#ran","syntology_url":"https://syntology.ai/paper/2402.17726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17726"}},"official":{"repos":["syp2ysy/vrp-sam"],"state":"official (archive's flag): 25 ran","n_ran":25,"n_constructed":4,"n_ran_no_instrument_failure":11,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/blo-sam-bi-level-optimization-based","slug":"blo-sam-bi-level-optimization-based","title":"BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of SAM","date":"2024-02-26","arxiv_id":"2402.16338","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"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) · 4 unverified","sample_list":"/paper/blo-sam-bi-level-optimization-based#ran","syntology_url":"https://syntology.ai/paper/2402.16338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16338"}},"official":{"repos":["importzl/blo-sam"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/placing-objects-in-context-via-inpainting-for","slug":"placing-objects-in-context-via-inpainting-for","title":"Placing Objects in Context via Inpainting for Out-of-distribution Segmentation","date":"2024-02-26","arxiv_id":"2402.16392","repositories_listed":1,"syntology":null},{"url":"/paper/spineps-automatic-whole-spine-segmentation-of","slug":"spineps-automatic-whole-spine-segmentation-of","title":"SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation","date":"2024-02-26","arxiv_id":"2402.16368","repositories_listed":1,"syntology":null},{"url":"/paper/un-sam-universal-prompt-free-segmentation-for","slug":"un-sam-universal-prompt-free-segmentation-for","title":"UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images","date":"2024-02-26","arxiv_id":"2402.16663","repositories_listed":1,"syntology":null},{"url":"/paper/increasing-sam-zero-shot-performance-on","slug":"increasing-sam-zero-shot-performance-on","title":"TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation","date":"2024-02-24","arxiv_id":"2402.15759","repositories_listed":1,"syntology":null},{"url":"/paper/gs-ema-integrating-gradient-surgery","slug":"gs-ema-integrating-gradient-surgery","title":"GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation","date":"2024-02-23","arxiv_id":"2402.15239","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-framework-uniting-visual-in-context","slug":"a-simple-framework-uniting-visual-in-context","title":"A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation","date":"2024-02-22","arxiv_id":"2402.14300","repositories_listed":1,"syntology":null},{"url":"/paper/subobject-level-image-tokenization","slug":"subobject-level-image-tokenization","title":"Subobject-level Image Tokenization","date":"2024-02-22","arxiv_id":"2402.14327","repositories_listed":1,"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":7,"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/subobject-level-image-tokenization#ran","syntology_url":"https://syntology.ai/paper/2402.14327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14327"}},"official":{"repos":["chendelong1999/subobjects"],"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/weaksam-segment-anything-meets-weakly","slug":"weaksam-segment-anything-meets-weakly","title":"WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition","date":"2024-02-22","arxiv_id":"2402.14812","repositories_listed":1,"syntology":null},{"url":"/paper/benchcloudvision-a-benchmark-analysis-of-deep","slug":"benchcloudvision-a-benchmark-analysis-of-deep","title":"BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery","date":"2024-02-21","arxiv_id":"2402.13918","repositories_listed":1,"syntology":null},{"url":"/paper/deisam-segment-anything-with-deictic","slug":"deisam-segment-anything-with-deictic","title":"DeiSAM: Segment Anything with Deictic Prompting","date":"2024-02-21","arxiv_id":"2402.14123","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/deisam-segment-anything-with-deictic#ran","syntology_url":"https://syntology.ai/paper/2402.14123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14123"}},"official":{"repos":["ml-research/deictic-segment-anything"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/wmh-seg-transformer-based-u-net-for-robust","slug":"wmh-seg-transformer-based-u-net-for-robust","title":"wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T","date":"2024-02-20","arxiv_id":"2402.12701","repositories_listed":1,"syntology":null},{"url":"/paper/3d-vascular-segmentation-supervised-by-2d","slug":"3d-vascular-segmentation-supervised-by-2d","title":"3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection","date":"2024-02-19","arxiv_id":"2402.12128","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-as-a-parsimonious","slug":"reinforcement-learning-as-a-parsimonious","title":"Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation","date":"2024-02-19","arxiv_id":"2402.11760","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/reinforcement-learning-as-a-parsimonious#ran","syntology_url":"https://syntology.ai/paper/2402.11760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11760"}},"official":{"repos":["scailab/paser"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/polypnextlstm-a-lightweight-and-fast-polyp","slug":"polypnextlstm-a-lightweight-and-fast-polyp","title":"PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM","date":"2024-02-18","arxiv_id":"2402.11585","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-aware-neural-radiance-fields-for","slug":"semantically-aware-neural-radiance-fields-for","title":"Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review","date":"2024-02-17","arxiv_id":"2402.11141","repositories_listed":1,"syntology":null},{"url":"/paper/floor-plan-image-segmentation-via-scribble","slug":"floor-plan-image-segmentation-via-scribble","title":"Floor Plan Image Segmentation Via Scribble-Based Semi-Weakly Supervised Learning: A Style and Category-Agnostic Approach","date":"2024-02-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/crop-and-couple-cardiac-image-segmentation","slug":"crop-and-couple-cardiac-image-segmentation","title":"Crop and Couple: cardiac image segmentation using interlinked specialist networks","date":"2024-02-14","arxiv_id":"2402.09156","repositories_listed":1,"syntology":null},{"url":"/paper/befunet-a-hybrid-cnn-transformer-architecture","slug":"befunet-a-hybrid-cnn-transformer-architecture","title":"BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation","date":"2024-02-13","arxiv_id":"2402.08793","repositories_listed":1,"syntology":null},{"url":"/paper/fess-loss-feature-enhanced-spatial","slug":"fess-loss-feature-enhanced-spatial","title":"FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis","date":"2024-02-13","arxiv_id":"2402.08582","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-based-fast-weak-supervision-and","slug":"scribble-based-fast-weak-supervision-and","title":"Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images","date":"2024-02-13","arxiv_id":"2402.08333","repositories_listed":1,"syntology":null},{"url":"/paper/complete-instances-mining-for-weakly-1","slug":"complete-instances-mining-for-weakly-1","title":"Complete Instances Mining for Weakly Supervised Instance Segmentation","date":"2024-02-12","arxiv_id":"2402.07633","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/complete-instances-mining-for-weakly-1#ran","syntology_url":"https://syntology.ai/paper/2402.07633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07633"}},"official":{"repos":["ZechengLi19/CIM"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/re-diffinet-modeling-discrepancies-in-tumor","slug":"re-diffinet-modeling-discrepancies-in-tumor","title":"Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models","date":"2024-02-12","arxiv_id":"2402.07354","repositories_listed":1,"syntology":null},{"url":"/paper/semi-mamba-unet-pixel-level-contrastive-cross","slug":"semi-mamba-unet-pixel-level-contrastive-cross","title":"Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation","date":"2024-02-11","arxiv_id":"2402.07245","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semi-mamba-unet-pixel-level-contrastive-cross#ran","syntology_url":"https://syntology.ai/paper/2402.07245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07245"}},"official":{"repos":["ziyangwang007/mamba-unet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-optimization-framework-for-processing-and","slug":"an-optimization-framework-for-processing-and","title":"An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation","date":"2024-02-10","arxiv_id":"2402.07008","repositories_listed":1,"syntology":null},{"url":"/paper/iris-sam-iris-segmentation-using-a","slug":"iris-sam-iris-segmentation-using-a","title":"Iris-SAM: Iris Segmentation Using a Foundation Model","date":"2024-02-09","arxiv_id":"2402.06497","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-synthetic-continual","slug":"privacy-preserving-synthetic-continual","title":"Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery","date":"2024-02-08","arxiv_id":"2402.05860","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-aware-contrastive-learning-for-semi","slug":"boundary-aware-contrastive-learning-for-semi","title":"Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation","date":"2024-02-07","arxiv_id":"2402.04756","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-unet-unet-like-pure-visual-mamba-for","slug":"mamba-unet-unet-like-pure-visual-mamba-for","title":"Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation","date":"2024-02-07","arxiv_id":"2402.05079","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mamba-unet-unet-like-pure-visual-mamba-for#ran","syntology_url":"https://syntology.ai/paper/2402.05079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05079"}},"official":{"repos":["ziyangwang007/mamba-unet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/polyp-ddpm-diffusion-based-semantic-polyp","slug":"polyp-ddpm-diffusion-based-semantic-polyp","title":"Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation","date":"2024-02-06","arxiv_id":"2402.04031","repositories_listed":1,"syntology":null},{"url":"/paper/reborn-reinforcement-learned-boundary","slug":"reborn-reinforcement-learned-boundary","title":"REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR","date":"2024-02-06","arxiv_id":"2402.03988","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/reborn-reinforcement-learned-boundary#ran","syntology_url":"https://syntology.ai/paper/2402.03988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03988"}},"official":{"repos":["andybi7676/reborn-uasr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sisp-a-benchmark-dataset-for-fine-grained","slug":"sisp-a-benchmark-dataset-for-fine-grained","title":"SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images","date":"2024-02-06","arxiv_id":"2402.03708","repositories_listed":1,"syntology":null},{"url":"/paper/ct-based-anatomical-segmentation-for-thoracic","slug":"ct-based-anatomical-segmentation-for-thoracic","title":"Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation","date":"2024-02-05","arxiv_id":"2402.03230","repositories_listed":1,"syntology":null},{"url":"/paper/rrwnet-recursive-refinement-network-for","slug":"rrwnet-recursive-refinement-network-for","title":"RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification","date":"2024-02-05","arxiv_id":"2402.03166","repositories_listed":1,"syntology":null},{"url":"/paper/sgs-slam-semantic-gaussian-splatting-for","slug":"sgs-slam-semantic-gaussian-splatting-for","title":"SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM","date":"2024-02-05","arxiv_id":"2402.03246","repositories_listed":1,"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":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) · 1 unverified","sample_list":"/paper/sgs-slam-semantic-gaussian-splatting-for#ran","syntology_url":"https://syntology.ai/paper/2402.03246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03246"}},"official":{"repos":["shuhongll/sgs-slam"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scribformer-transformer-makes-cnn-work-better","slug":"scribformer-transformer-makes-cnn-work-better","title":"ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation","date":"2024-02-03","arxiv_id":"2402.02029","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multimodal-fusion-of-data-with","slug":"deep-multimodal-fusion-of-data-with","title":"Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks","date":"2024-02-02","arxiv_id":"2402.01311","repositories_listed":1,"syntology":null},{"url":"/paper/vision-mae-a-foundation-model-for-medical","slug":"vision-mae-a-foundation-model-for-medical","title":"VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification","date":"2024-02-01","arxiv_id":"2402.01034","repositories_listed":1,"syntology":null},{"url":"/paper/convolution-meets-lora-parameter-efficient","slug":"convolution-meets-lora-parameter-efficient","title":"Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model","date":"2024-01-31","arxiv_id":"2401.17868","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convolution-meets-lora-parameter-efficient#ran","syntology_url":"https://syntology.ai/paper/2401.17868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17868"}},"official":{"repos":["autogluon/autogluon"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/datacube-segmentation-via-deep-spectral","slug":"datacube-segmentation-via-deep-spectral","title":"Datacube segmentation via Deep Spectral Clustering","date":"2024-01-31","arxiv_id":"2401.17695","repositories_listed":1,"syntology":null},{"url":"/paper/hi-sam-marrying-segment-anything-model-for","slug":"hi-sam-marrying-segment-anything-model-for","title":"Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation","date":"2024-01-31","arxiv_id":"2401.17904","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hi-sam-marrying-segment-anything-model-for#ran","syntology_url":"https://syntology.ai/paper/2401.17904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17904"}},"official":{"repos":["ymy-k/hi-sam"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-anything-in-3d-gaussians","slug":"semantic-anything-in-3d-gaussians","title":"SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition","date":"2024-01-31","arxiv_id":"2401.17857","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semantic-anything-in-3d-gaussians#ran","syntology_url":"https://syntology.ai/paper/2401.17857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17857"}},"official":{"repos":["xuhu0529/sags"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/h-synex-using-synthetic-images-and-ultra-high","slug":"h-synex-using-synthetic-images-and-ultra-high","title":"H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation","date":"2024-01-30","arxiv_id":"2401.17104","repositories_listed":1,"syntology":null},{"url":"/paper/cyto-r-cnn-and-cytonuke-dataset-towards","slug":"cyto-r-cnn-and-cytonuke-dataset-towards","title":"Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images","date":"2024-01-28","arxiv_id":"2401.15638","repositories_listed":1,"syntology":null},{"url":"/paper/paratranscnn-parallelized-transcnn-encoder","slug":"paratranscnn-parallelized-transcnn-encoder","title":"ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation","date":"2024-01-27","arxiv_id":"2401.15307","repositories_listed":1,"syntology":null},{"url":"/paper/vanishing-point-guided-video-semantic","slug":"vanishing-point-guided-video-semantic","title":"Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes","date":"2024-01-27","arxiv_id":"2401.15261","repositories_listed":1,"syntology":null},{"url":"/paper/inconsistency-masks-removing-the-uncertainty","slug":"inconsistency-masks-removing-the-uncertainty","title":"Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs","date":"2024-01-25","arxiv_id":"2401.14387","repositories_listed":1,"syntology":null},{"url":"/paper/neighbor-aware-calibration-of-segmentation","slug":"neighbor-aware-calibration-of-segmentation","title":"Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints","date":"2024-01-25","arxiv_id":"2401.14487","repositories_listed":1,"syntology":null},{"url":"/paper/pix2gestalt-amodal-segmentation-by","slug":"pix2gestalt-amodal-segmentation-by","title":"pix2gestalt: Amodal Segmentation by Synthesizing Wholes","date":"2024-01-25","arxiv_id":"2401.14398","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":2,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 3 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pix2gestalt-amodal-segmentation-by#ran","syntology_url":"https://syntology.ai/paper/2401.14398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14398"}},"official":{"repos":["cvlab-columbia/pix2gestalt"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vivim-a-video-vision-mamba-for-medical-video","slug":"vivim-a-video-vision-mamba-for-medical-video","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","date":"2024-01-25","arxiv_id":"2401.14168","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"10 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/vivim-a-video-vision-mamba-for-medical-video#ran","syntology_url":"https://syntology.ai/paper/2401.14168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14168"}},"official":{"repos":["scott-yjyang/vivim"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/wal-net-weakly-supervised-auxiliary-task","slug":"wal-net-weakly-supervised-auxiliary-task","title":"WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification","date":"2024-01-25","arxiv_id":"2401.13998","repositories_listed":1,"syntology":null},{"url":"/paper/sednet-shallow-encoder-decoder-network-for","slug":"sednet-shallow-encoder-decoder-network-for","title":"SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation","date":"2024-01-24","arxiv_id":"2401.13403","repositories_listed":1,"syntology":null},{"url":"/paper/tissue-cross-section-and-pen-marking","slug":"tissue-cross-section-and-pen-marking","title":"Tissue Cross-Section and Pen Marking Segmentation in Whole Slide Images","date":"2024-01-24","arxiv_id":"2401.13511","repositories_listed":1,"syntology":null},{"url":"/paper/tyche-stochastic-in-context-learning-for","slug":"tyche-stochastic-in-context-learning-for","title":"Tyche: Stochastic In-Context Learning for Medical Image Segmentation","date":"2024-01-24","arxiv_id":"2401.13650","repositories_listed":1,"syntology":null},{"url":"/paper/cis-unet-multi-class-segmentation-of-the","slug":"cis-unet-multi-class-segmentation-of-the","title":"CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention","date":"2024-01-23","arxiv_id":"2401.13049","repositories_listed":1,"syntology":null},{"url":"/paper/clipsam-clip-and-sam-collaboration-for-zero","slug":"clipsam-clip-and-sam-collaboration-for-zero","title":"ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation","date":"2024-01-23","arxiv_id":"2401.12665","repositories_listed":1,"syntology":null},{"url":"/paper/datus-2-data-driven-unsupervised-semantic","slug":"datus-2-data-driven-unsupervised-semantic","title":"DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer","date":"2024-01-23","arxiv_id":"2401.12820","repositories_listed":1,"syntology":null},{"url":"/paper/mast-video-polyp-segmentation-with-a-mixture","slug":"mast-video-polyp-segmentation-with-a-mixture","title":"MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer","date":"2024-01-23","arxiv_id":"2401.12439","repositories_listed":1,"syntology":null},{"url":"/paper/pa-sam-prompt-adapter-sam-for-high-quality","slug":"pa-sam-prompt-adapter-sam-for-high-quality","title":"PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation","date":"2024-01-23","arxiv_id":"2401.13051","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":15,"phrase":"9 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/pa-sam-prompt-adapter-sam-for-high-quality#ran","syntology_url":"https://syntology.ai/paper/2401.13051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.13051"}},"official":{"repos":["xzz2/pa-sam"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/close-a-3d-clothing-segmentation-dataset-and","slug":"close-a-3d-clothing-segmentation-dataset-and","title":"CloSe: A 3D Clothing Segmentation Dataset and Model","date":"2024-01-22","arxiv_id":"2401.12051","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-simple-open-vocabulary-semantic","slug":"exploring-simple-open-vocabulary-semantic","title":"Exploring Simple Open-Vocabulary Semantic Segmentation","date":"2024-01-22","arxiv_id":"2401.12217","repositories_listed":1,"syntology":null},{"url":"/paper/rta-former-reverse-transformer-attention-for","slug":"rta-former-reverse-transformer-attention-for","title":"RTA-Former: Reverse Transformer Attention for Polyp Segmentation","date":"2024-01-22","arxiv_id":"2401.11671","repositories_listed":1,"syntology":null},{"url":"/paper/semples-semantic-prompt-learning-for-weakly","slug":"semples-semantic-prompt-learning-for-weakly","title":"Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation","date":"2024-01-22","arxiv_id":"2401.11791","repositories_listed":1,"syntology":null},{"url":"/paper/sfc-shared-feature-calibration-in-weakly","slug":"sfc-shared-feature-calibration-in-weakly","title":"SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation","date":"2024-01-22","arxiv_id":"2401.11719","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sfc-shared-feature-calibration-in-weakly#ran","syntology_url":"https://syntology.ai/paper/2401.11719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11719"}},"official":{"repos":["barrett-python/sfc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-novel-benchmark-for-few-shot-semantic","slug":"a-novel-benchmark-for-few-shot-semantic","title":"A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models","date":"2024-01-20","arxiv_id":"2401.11311","repositories_listed":1,"syntology":null},{"url":"/paper/susceptibility-of-adversarial-attack-on","slug":"susceptibility-of-adversarial-attack-on","title":"Susceptibility of Adversarial Attack on Medical Image Segmentation Models","date":"2024-01-20","arxiv_id":"2401.11224","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-deep-learning-approach-featuring","slug":"a-novel-deep-learning-approach-featuring","title":"A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking","date":"2024-01-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-latent-diffusion-approach-for","slug":"a-simple-latent-diffusion-approach-for","title":"A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting","date":"2024-01-18","arxiv_id":"2401.10227","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-simple-latent-diffusion-approach-for#ran","syntology_url":"https://syntology.ai/paper/2401.10227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10227"}},"official":{"repos":["segments-ai/latent-diffusion-segmentation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/enhanced-automated-quality-assessment-network","slug":"enhanced-automated-quality-assessment-network","title":"Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery","date":"2024-01-18","arxiv_id":"2401.09828","repositories_listed":1,"syntology":null},{"url":"/paper/omg-seg-is-one-model-good-enough-for-all","slug":"omg-seg-is-one-model-good-enough-for-all","title":"OMG-Seg: Is One Model Good Enough For All Segmentation?","date":"2024-01-18","arxiv_id":"2401.10229","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/omg-seg-is-one-model-good-enough-for-all#ran","syntology_url":"https://syntology.ai/paper/2401.10229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10229"}},"official":{"repos":["lxtgh/omg-seg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rap-sam-towards-real-time-all-purpose-segment","slug":"rap-sam-towards-real-time-all-purpose-segment","title":"RAP-SAM: Towards Real-Time All-Purpose Segment Anything","date":"2024-01-18","arxiv_id":"2401.10228","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rap-sam-towards-real-time-all-purpose-segment#ran","syntology_url":"https://syntology.ai/paper/2401.10228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10228"}},"official":{"repos":["xushilin1/rap-sam"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ct-liver-segmentation-via-pvt-based-encoding","slug":"ct-liver-segmentation-via-pvt-based-encoding","title":"CT Liver Segmentation via PVT-based Encoding and Refined Decoding","date":"2024-01-17","arxiv_id":"2401.09630","repositories_listed":1,"syntology":null},{"url":"/paper/symtc-a-symbiotic-transformer-cnn-net-for","slug":"symtc-a-symbiotic-transformer-cnn-net-for","title":"SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI","date":"2024-01-17","arxiv_id":"2401.09627","repositories_listed":1,"syntology":null},{"url":"/paper/completely-occluded-and-dense-object-instance","slug":"completely-occluded-and-dense-object-instance","title":"OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts","date":"2024-01-16","arxiv_id":"2401.08174","repositories_listed":1,"syntology":null},{"url":"/paper/generative-denoise-distillation-simple","slug":"generative-denoise-distillation-simple","title":"Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction","date":"2024-01-16","arxiv_id":"2401.08332","repositories_listed":1,"syntology":null},{"url":"/paper/samf-small-area-aware-multi-focus-image","slug":"samf-small-area-aware-multi-focus-image","title":"SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection","date":"2024-01-16","arxiv_id":"2401.08357","repositories_listed":1,"syntology":null},{"url":"/paper/training-and-comparison-of-nnu-net-and","slug":"training-and-comparison-of-nnu-net-and","title":"Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors","date":"2024-01-16","arxiv_id":"2401.08404","repositories_listed":1,"syntology":null},{"url":"/paper/bonus-boundary-mining-for-nuclei-segmentation","slug":"bonus-boundary-mining-for-nuclei-segmentation","title":"BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels","date":"2024-01-15","arxiv_id":"2401.07437","repositories_listed":1,"syntology":null},{"url":"/paper/pmfsnet-polarized-multi-scale-feature-self","slug":"pmfsnet-polarized-multi-scale-feature-self","title":"PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation","date":"2024-01-15","arxiv_id":"2401.07579","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-traditional-approaches-multi-task","slug":"beyond-traditional-approaches-multi-task","title":"Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis","date":"2024-01-14","arxiv_id":"2401.07326","repositories_listed":1,"syntology":null},{"url":"/paper/development-of-rlk-unet-a-clinically","slug":"development-of-rlk-unet-a-clinically","title":"Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment","date":"2024-01-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-automated-framework-for-brain-vessel","slug":"an-automated-framework-for-brain-vessel","title":"An automated framework for brain vessel centerline extraction from CTA images","date":"2024-01-13","arxiv_id":"2401.07041","repositories_listed":1,"syntology":null}],"record_sha256":"1555978fe4486be759684faa573dacef47f167bc4a277d7a939b00cbdc903fb5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}