{"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/instance-segmentation/papers/6","list_of":"/task/instance-segmentation","task":"Instance 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":6,"pages_in_order":23,"rows_per_page":100,"rows":[501,600],"of":2262,"counts":{"archive_papers_tagged":2262,"with_a_code_link":1158,"where_syntology_ran_a_sample":324,"not_listed_spam_title":0,"listed":2262,"listed_where_code_ran":324,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":289,"every_run_a_failure_of_syntologys_instrument":35,"listed_with_a_run_with_no_instrument_failure":289,"listed_every_run_a_failure_of_syntologys_instrument":35,"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/instance-segmentation","prev":"/task/instance-segmentation/papers/5","next":"/task/instance-segmentation/papers/7","papers":[{"url":"/paper/diod-self-distillation-meets-object-discovery","slug":"diod-self-distillation-meets-object-discovery","title":"DIOD: Self-Distillation Meets Object Discovery","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-improved-baseline-for-reasoning","slug":"an-improved-baseline-for-reasoning","title":"LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model","date":"2023-12-28","arxiv_id":"2312.17240","repositories_listed":1,"syntology":{"n":14,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 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; 3 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/an-improved-baseline-for-reasoning#ran","syntology_url":"https://syntology.ai/paper/2312.17240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17240"}},"official":null}},{"url":"/paper/semantic-aware-sam-for-point-prompted","slug":"semantic-aware-sam-for-point-prompted","title":"Semantic-aware SAM for Point-Prompted Instance Segmentation","date":"2023-12-26","arxiv_id":"2312.15895","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/semantic-aware-sam-for-point-prompted#ran","syntology_url":"https://syntology.ai/paper/2312.15895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15895"}},"official":{"repos":["zhaoyangwei123/sapnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cached-transformers-improving-transformers","slug":"cached-transformers-improving-transformers","title":"Cached Transformers: Improving Transformers with Differentiable Memory Cache","date":"2023-12-20","arxiv_id":"2312.12742","repositories_listed":1,"syntology":null},{"url":"/paper/dvis-improved-decoupled-framework-for","slug":"dvis-improved-decoupled-framework-for","title":"DVIS++: Improved Decoupled Framework for Universal Video Segmentation","date":"2023-12-20","arxiv_id":"2312.13305","repositories_listed":1,"syntology":null},{"url":"/paper/segrefiner-towards-model-agnostic-1","slug":"segrefiner-towards-model-agnostic-1","title":"SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process","date":"2023-12-19","arxiv_id":"2312.12425","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/segrefiner-towards-model-agnostic-1#ran","syntology_url":"https://syntology.ai/paper/2312.12425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12425"}},"official":{"repos":["mengyuwang826/segrefiner"],"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/the-endoscapes-dataset-for-surgical-scene","slug":"the-endoscapes-dataset-for-surgical-scene","title":"The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark","date":"2023-12-19","arxiv_id":"2312.12429","repositories_listed":1,"syntology":null},{"url":"/paper/spherical-mask-coarse-to-fine-3d-point-cloud","slug":"spherical-mask-coarse-to-fine-3d-point-cloud","title":"Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation","date":"2023-12-18","arxiv_id":"2312.11269","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"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 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) · 3 unverified","sample_list":"/paper/spherical-mask-coarse-to-fine-3d-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2312.11269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11269"}},"official":{"repos":["yunshin/SphericalMask"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/open3dis-open-vocabulary-3d-instance","slug":"open3dis-open-vocabulary-3d-instance","title":"Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance","date":"2023-12-17","arxiv_id":"2312.10671","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/open3dis-open-vocabulary-3d-instance#ran","syntology_url":"https://syntology.ai/paper/2312.10671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.10671"}},"official":{"repos":["VinAIResearch/Open3DIS"],"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/semantic-aware-autoregressive-image-modeling","slug":"semantic-aware-autoregressive-image-modeling","title":"Semantic-Aware Autoregressive Image Modeling for Visual Representation Learning","date":"2023-12-16","arxiv_id":"2312.10457","repositories_listed":1,"syntology":null},{"url":"/paper/cattleeyeview-a-multi-task-top-down-view","slug":"cattleeyeview-a-multi-task-top-down-view","title":"CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming","date":"2023-12-14","arxiv_id":"2312.08764","repositories_listed":1,"syntology":null},{"url":"/paper/general-object-foundation-model-for-images","slug":"general-object-foundation-model-for-images","title":"General Object Foundation Model for Images and Videos at Scale","date":"2023-12-14","arxiv_id":"2312.09158","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/general-object-foundation-model-for-images#ran","syntology_url":"https://syntology.ai/paper/2312.09158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09158"}},"official":{"repos":["FoundationVision/GLEE"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/automated-behavioral-analysis-using-instance","slug":"automated-behavioral-analysis-using-instance","title":"Automated Behavioral Analysis Using Instance Segmentation","date":"2023-12-12","arxiv_id":"2312.07723","repositories_listed":1,"syntology":null},{"url":"/paper/maxq-multi-axis-query-for-n-m-sparsity","slug":"maxq-multi-axis-query-for-n-m-sparsity","title":"MaxQ: Multi-Axis Query for N:M Sparsity Network","date":"2023-12-12","arxiv_id":"2312.07061","repositories_listed":1,"syntology":null},{"url":"/paper/mwsis-multimodal-weakly-supervised-instance","slug":"mwsis-multimodal-weakly-supervised-instance","title":"MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving","date":"2023-12-12","arxiv_id":"2312.06988","repositories_listed":1,"syntology":null},{"url":"/paper/asf-yolo-a-novel-yolo-model-with-attentional","slug":"asf-yolo-a-novel-yolo-model-with-attentional","title":"ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation","date":"2023-12-11","arxiv_id":"2312.06458","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/asf-yolo-a-novel-yolo-model-with-attentional#ran","syntology_url":"https://syntology.ai/paper/2312.06458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06458"}},"official":{"repos":["mkang315/asf-yolo"],"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/tmt-vis-taxonomy-aware-multi-dataset-joint-1","slug":"tmt-vis-taxonomy-aware-multi-dataset-joint-1","title":"TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation","date":"2023-12-11","arxiv_id":"2312.06630","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/tmt-vis-taxonomy-aware-multi-dataset-joint-1#ran","syntology_url":"https://syntology.ai/paper/2312.06630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06630"}},"official":{"repos":["rkzheng99/tmt-vis"],"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/visage-video-instance-segmentation-with","slug":"visage-video-instance-segmentation-with","title":"VISAGE: Video Instance Segmentation with Appearance-Guided Enhancement","date":"2023-12-08","arxiv_id":"2312.04885","repositories_listed":1,"syntology":null},{"url":"/paper/panoptica-instance-wise-evaluation-of-3d","slug":"panoptica-instance-wise-evaluation-of-3d","title":"Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps","date":"2023-12-05","arxiv_id":"2312.02608","repositories_listed":1,"syntology":null},{"url":"/paper/partslip-enhancing-low-shot-3d-part","slug":"partslip-enhancing-low-shot-3d-part","title":"PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation","date":"2023-12-05","arxiv_id":"2312.03015","repositories_listed":1,"syntology":{"n":17,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/partslip-enhancing-low-shot-3d-part#ran","syntology_url":"https://syntology.ai/paper/2312.03015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03015"}},"official":{"repos":["zyc00/partslip2"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-data-efficient-framework-for-robotics-large","slug":"a-data-efficient-framework-for-robotics-large","title":"A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing","date":"2023-12-03","arxiv_id":"2312.02208","repositories_listed":1,"syntology":null},{"url":"/paper/efficientsam-leveraged-masked-image","slug":"efficientsam-leveraged-masked-image","title":"EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything","date":"2023-12-01","arxiv_id":"2312.00863","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-label-efficient-3d-scene-parsing","slug":"generalized-label-efficient-3d-scene-parsing","title":"Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment","date":"2023-12-01","arxiv_id":"2312.00663","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-proposal-based-object-detection","slug":"revisiting-proposal-based-object-detection","title":"Union-over-Intersections: Object Detection beyond Winner-Takes-All","date":"2023-11-30","arxiv_id":"2311.18512","repositories_listed":1,"syntology":null},{"url":"/paper/continual-learning-for-image-segmentation","slug":"continual-learning-for-image-segmentation","title":"Continual Learning for Image Segmentation with Dynamic Query","date":"2023-11-29","arxiv_id":"2311.17450","repositories_listed":1,"syntology":null},{"url":"/paper/sam-6d-segment-anything-model-meets-zero-shot","slug":"sam-6d-segment-anything-model-meets-zero-shot","title":"SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation","date":"2023-11-27","arxiv_id":"2311.15707","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-the-power-of-prompt-driven-nucleus","slug":"unleashing-the-power-of-prompt-driven-nucleus","title":"Unleashing the Power of Prompt-driven Nucleus Instance Segmentation","date":"2023-11-27","arxiv_id":"2311.15939","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unleashing-the-power-of-prompt-driven-nucleus#ran","syntology_url":"https://syntology.ai/paper/2311.15939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15939"}},"official":{"repos":["windygoo/promptnucseg"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adapter-is-all-you-need-for-tuning-visual","slug":"adapter-is-all-you-need-for-tuning-visual","title":"Adapter is All You Need for Tuning Visual Tasks","date":"2023-11-25","arxiv_id":"2311.15010","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-planar-reconstruction-with-feature","slug":"multi-task-planar-reconstruction-with-feature","title":"Multi-task Planar Reconstruction with Feature Warping Guidance","date":"2023-11-25","arxiv_id":"2311.14981","repositories_listed":1,"syntology":null},{"url":"/paper/text-and-click-inputs-for-unambiguous-open","slug":"text-and-click-inputs-for-unambiguous-open","title":"Text and Click inputs for unambiguous open vocabulary instance segmentation","date":"2023-11-24","arxiv_id":"2311.14822","repositories_listed":1,"syntology":null},{"url":"/paper/low-latency-instance-segmentation-by","slug":"low-latency-instance-segmentation-by","title":"Low Latency Instance Segmentation by Continuous Clustering for LiDAR Sensors","date":"2023-11-23","arxiv_id":"2311.13976","repositories_listed":1,"syntology":null},{"url":"/paper/hover-unet-accelerating-hovernet-with-unet","slug":"hover-unet-accelerating-hovernet-with-unet","title":"HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation","date":"2023-11-21","arxiv_id":"2311.12553","repositories_listed":1,"syntology":null},{"url":"/paper/pwiseg-point-based-weakly-supervised-instance","slug":"pwiseg-point-based-weakly-supervised-instance","title":"PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments","date":"2023-11-16","arxiv_id":"2311.09819","repositories_listed":1,"syntology":null},{"url":"/paper/video-instance-matting","slug":"video-instance-matting","title":"Video Instance Matting","date":"2023-11-07","arxiv_id":"2311.04212","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-enhancement-of-low-light-image","slug":"zero-shot-enhancement-of-low-light-image","title":"Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition","date":"2023-11-06","arxiv_id":"2311.02995","repositories_listed":1,"syntology":null},{"url":"/paper/audio-visual-instance-segmentation","slug":"audio-visual-instance-segmentation","title":"Audio-Visual Instance Segmentation","date":"2023-10-28","arxiv_id":"2310.18709","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-under-occlusions-via","slug":"instance-segmentation-under-occlusions-via","title":"Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation","date":"2023-10-27","arxiv_id":"2310.17949","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-approach-to-teeth","slug":"a-deep-learning-approach-to-teeth","title":"A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays","date":"2023-10-26","arxiv_id":"2310.17176","repositories_listed":1,"syntology":null},{"url":"/paper/label-efficient-segmentation-via-affinity-1","slug":"label-efficient-segmentation-via-affinity-1","title":"Label-efficient Segmentation via Affinity Propagation","date":"2023-10-16","arxiv_id":"2310.10533","repositories_listed":1,"syntology":null},{"url":"/paper/robollm-robotic-vision-tasks-grounded-on","slug":"robollm-robotic-vision-tasks-grounded-on","title":"RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models","date":"2023-10-16","arxiv_id":"2310.10221","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-object-centric-1","slug":"unsupervised-learning-of-object-centric-1","title":"Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images","date":"2023-10-12","arxiv_id":"2310.08501","repositories_listed":1,"syntology":null},{"url":"/paper/relational-prior-knowledge-graphs-for","slug":"relational-prior-knowledge-graphs-for","title":"Relational Prior Knowledge Graphs for Detection and Instance Segmentation","date":"2023-10-11","arxiv_id":"2310.07573","repositories_listed":1,"syntology":null},{"url":"/paper/evit-an-eagle-vision-transformer-with-bi","slug":"evit-an-eagle-vision-transformer-with-bi","title":"EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention","date":"2023-10-10","arxiv_id":"2310.06629","repositories_listed":1,"syntology":null},{"url":"/paper/convnextv2-fusion-with-mask-r-cnn-for","slug":"convnextv2-fusion-with-mask-r-cnn-for","title":"ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis","date":"2023-10-07","arxiv_id":"2310.04749","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-refinement-of-buildings","slug":"zero-shot-refinement-of-buildings","title":"Zero-Shot Refinement of Buildings' Segmentation Models using SAM","date":"2023-10-03","arxiv_id":"2310.01845","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-contextualized","slug":"self-supervised-learning-of-contextualized","title":"Self-supervised Learning of Contextualized Local Visual Embeddings","date":"2023-10-01","arxiv_id":"2310.00527","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-of-contextualized#ran","syntology_url":"https://syntology.ai/paper/2310.00527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00527"}},"official":{"repos":["sthalles/clove"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mask4d-mask-transformer-for-4d-panoptic","slug":"mask4d-mask-transformer-for-4d-panoptic","title":"Mask4Former: Mask Transformer for 4D Panoptic Segmentation","date":"2023-09-28","arxiv_id":"2309.16133","repositories_listed":1,"syntology":null},{"url":"/paper/mocae-mixture-of-calibrated-experts","slug":"mocae-mixture-of-calibrated-experts","title":"MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection","date":"2023-09-26","arxiv_id":"2309.14976","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mocae-mixture-of-calibrated-experts#ran","syntology_url":"https://syntology.ai/paper/2309.14976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14976"}},"official":{"repos":["fiveai/MoCaE"],"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/3d-indoor-instance-segmentation-in-an-open-1","slug":"3d-indoor-instance-segmentation-in-an-open-1","title":"3D Indoor Instance Segmentation in an Open-World","date":"2023-09-25","arxiv_id":"2309.14338","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/3d-indoor-instance-segmentation-in-an-open-1#ran","syntology_url":"https://syntology.ai/paper/2309.14338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14338"}},"official":{"repos":["aminebdj/3d-owis"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/clusterformer-clustering-as-a-universal","slug":"clusterformer-clustering-as-a-universal","title":"ClusterFormer: Clustering As A Universal Visual Learner","date":"2023-09-22","arxiv_id":"2309.13196","repositories_listed":1,"syntology":null},{"url":"/paper/mosaicfusion-diffusion-models-as-data","slug":"mosaicfusion-diffusion-models-as-data","title":"MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation","date":"2023-09-22","arxiv_id":"2309.13042","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"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) · 1 unverified","sample_list":"/paper/mosaicfusion-diffusion-models-as-data#ran","syntology_url":"https://syntology.ai/paper/2309.13042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13042"}},"official":{"repos":["jiahao000/mosaicfusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/neurallabeling-a-versatile-toolset-for","slug":"neurallabeling-a-versatile-toolset-for","title":"NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields","date":"2023-09-21","arxiv_id":"2309.11966","repositories_listed":1,"syntology":null},{"url":"/paper/tcovis-temporally-consistent-online-video","slug":"tcovis-temporally-consistent-online-video","title":"TCOVIS: Temporally Consistent Online Video Instance Segmentation","date":"2023-09-21","arxiv_id":"2309.11857","repositories_listed":1,"syntology":null},{"url":"/paper/rmt-retentive-networks-meet-vision","slug":"rmt-retentive-networks-meet-vision","title":"RMT: Retentive Networks Meet Vision Transformers","date":"2023-09-20","arxiv_id":"2309.11523","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/rmt-retentive-networks-meet-vision#ran","syntology_url":"https://syntology.ai/paper/2309.11523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11523"}},"official":{"repos":["qhfan/RMT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/drawing-the-same-bounding-box-twice-coping","slug":"drawing-the-same-bounding-box-twice-coping","title":"Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels","date":"2023-09-18","arxiv_id":"2309.09742","repositories_listed":1,"syntology":null},{"url":"/paper/omnilrs-a-photorealistic-simulator-for-lunar","slug":"omnilrs-a-photorealistic-simulator-for-lunar","title":"OmniLRS: A Photorealistic Simulator for Lunar Robotics","date":"2023-09-16","arxiv_id":"2309.08997","repositories_listed":1,"syntology":null},{"url":"/paper/treelearn-a-comprehensive-deep-learning","slug":"treelearn-a-comprehensive-deep-learning","title":"TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds","date":"2023-09-15","arxiv_id":"2309.08471","repositories_listed":1,"syntology":null},{"url":"/paper/x-pdnet-accurate-joint-plane-instance","slug":"x-pdnet-accurate-joint-plane-instance","title":"X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary Correction","date":"2023-09-15","arxiv_id":"2309.08424","repositories_listed":1,"syntology":null},{"url":"/paper/nucleus-aware-self-supervised-pretraining","slug":"nucleus-aware-self-supervised-pretraining","title":"Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images","date":"2023-09-14","arxiv_id":"2309.07394","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automated-scan-to-bim-via-point-cloud","slug":"fully-automated-scan-to-bim-via-point-cloud","title":"Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation","date":"2023-09-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-content-based-pixel-retrieval-in","slug":"towards-content-based-pixel-retrieval-in","title":"Towards Content-based Pixel Retrieval in Revisited Oxford and Paris","date":"2023-09-11","arxiv_id":"2309.05438","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/towards-content-based-pixel-retrieval-in#ran","syntology_url":"https://syntology.ai/paper/2309.05438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05438"}},"official":{"repos":["anguoyuan/pixel_retrieval-segmented_instance_retrieval"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sortedap-rethinking-evaluation-metrics-for","slug":"sortedap-rethinking-evaluation-metrics-for","title":"SortedAP: Rethinking evaluation metrics for instance segmentation","date":"2023-09-09","arxiv_id":"2309.04887","repositories_listed":1,"syntology":null},{"url":"/paper/dat-spatially-dynamic-vision-transformer-with","slug":"dat-spatially-dynamic-vision-transformer-with","title":"DAT++: Spatially Dynamic Vision Transformer with Deformable Attention","date":"2023-09-04","arxiv_id":"2309.01430","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dat-spatially-dynamic-vision-transformer-with#ran","syntology_url":"https://syntology.ai/paper/2309.01430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01430"}},"official":{"repos":["leaplabthu/dat"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mask-attention-free-transformer-for-3d","slug":"mask-attention-free-transformer-for-3d","title":"Mask-Attention-Free Transformer for 3D Instance Segmentation","date":"2023-09-04","arxiv_id":"2309.01692","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":10,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mask-attention-free-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2309.01692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01692"}},"official":{"repos":["dvlab-research/mask-attention-free-transformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/openins3d-snap-and-lookup-for-3d-open","slug":"openins3d-snap-and-lookup-for-3d-open","title":"OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation","date":"2023-09-01","arxiv_id":"2309.00616","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/openins3d-snap-and-lookup-for-3d-open#ran","syntology_url":"https://syntology.ai/paper/2309.00616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00616"}},"official":{"repos":["Pointcept/OpenIns3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ref-diff-zero-shot-referring-image","slug":"ref-diff-zero-shot-referring-image","title":"Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models","date":"2023-08-31","arxiv_id":"2308.16777","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-upsample-by-learning-to-sample","slug":"learning-to-upsample-by-learning-to-sample","title":"Learning to Upsample by Learning to Sample","date":"2023-08-29","arxiv_id":"2308.15085","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/learning-to-upsample-by-learning-to-sample#ran","syntology_url":"https://syntology.ai/paper/2308.15085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15085"}},"official":{"repos":["tiny-smart/dysample"],"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/1st-place-solution-for-the-5th-lsvos","slug":"1st-place-solution-for-the-5th-lsvos","title":"1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation","date":"2023-08-28","arxiv_id":"2308.14392","repositories_listed":1,"syntology":null},{"url":"/paper/videocutler-surprisingly-simple-unsupervised","slug":"videocutler-surprisingly-simple-unsupervised","title":"VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation","date":"2023-08-28","arxiv_id":"2308.14710","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/videocutler-surprisingly-simple-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2308.14710","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14710"}},"official":{"repos":["facebookresearch/cutler"],"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/segment-anything-for-microscopy","slug":"segment-anything-for-microscopy","title":"Segment Anything for Microscopy","date":"2023-08-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/eosinophils-instance-object-segmentation-on","slug":"eosinophils-instance-object-segmentation-on","title":"Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation","date":"2023-08-17","arxiv_id":"2308.08974","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-automatic-m-mode-echocardiography","slug":"real-time-automatic-m-mode-echocardiography","title":"Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels","date":"2023-08-15","arxiv_id":"2308.07717","repositories_listed":1,"syntology":null},{"url":"/paper/a-one-stop-3d-target-reconstruction-and","slug":"a-one-stop-3d-target-reconstruction-and","title":"A One Stop 3D Target Reconstruction and multilevel Segmentation Method","date":"2023-08-14","arxiv_id":"2308.06974","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-query-based-paradigm-for","slug":"a-unified-query-based-paradigm-for","title":"A Unified Query-based Paradigm for Camouflaged Instance Segmentation","date":"2023-08-14","arxiv_id":"2308.07392","repositories_listed":1,"syntology":null},{"url":"/paper/segprompt-boosting-open-world-segmentation","slug":"segprompt-boosting-open-world-segmentation","title":"SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning","date":"2023-08-12","arxiv_id":"2308.06531","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/segprompt-boosting-open-world-segmentation#ran","syntology_url":"https://syntology.ai/paper/2308.06531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06531"}},"official":{"repos":["aim-uofa/segprompt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/datasetdm-synthesizing-data-with-perception-1","slug":"datasetdm-synthesizing-data-with-perception-1","title":"DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models","date":"2023-08-11","arxiv_id":"2308.06160","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"9 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/datasetdm-synthesizing-data-with-perception-1#ran","syntology_url":"https://syntology.ai/paper/2308.06160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06160"}},"official":{"repos":["showlab/datasetdm"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/foodsam-any-food-segmentation","slug":"foodsam-any-food-segmentation","title":"FoodSAM: Any Food Segmentation","date":"2023-08-11","arxiv_id":"2308.05938","repositories_listed":1,"syntology":null},{"url":"/paper/pseudo-label-alignment-for-semi-supervised","slug":"pseudo-label-alignment-for-semi-supervised","title":"Pseudo-label Alignment for Semi-supervised Instance Segmentation","date":"2023-08-10","arxiv_id":"2308.05359","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-morphological","slug":"deep-learning-for-morphological","title":"Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels","date":"2023-08-09","arxiv_id":"2308.05166","repositories_listed":1,"syntology":null},{"url":"/paper/dit-efficient-vision-transformers-with","slug":"dit-efficient-vision-transformers-with","title":"DiT: Efficient Vision Transformers with Dynamic Token Routing","date":"2023-08-07","arxiv_id":"2308.03409","repositories_listed":1,"syntology":null},{"url":"/paper/guided-distillation-for-semi-supervised","slug":"guided-distillation-for-semi-supervised","title":"Guided Distillation for Semi-Supervised Instance Segmentation","date":"2023-08-03","arxiv_id":"2308.02668","repositories_listed":1,"syntology":null},{"url":"/paper/lidar-camera-panoptic-segmentation-via","slug":"lidar-camera-panoptic-segmentation-via","title":"LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment","date":"2023-08-03","arxiv_id":"2308.01686","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/lidar-camera-panoptic-segmentation-via#ran","syntology_url":"https://syntology.ai/paper/2308.01686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01686"}},"official":{"repos":["zhangzw12319/lcps"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/nuinsseg-a-fully-annotated-dataset-for-nuclei","slug":"nuinsseg-a-fully-annotated-dataset-for-nuclei","title":"NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images","date":"2023-08-03","arxiv_id":"2308.01760","repositories_listed":1,"syntology":null},{"url":"/paper/ugains-uncertainty-guided-anomaly-instance","slug":"ugains-uncertainty-guided-anomaly-instance","title":"UGainS: Uncertainty Guided Anomaly Instance Segmentation","date":"2023-08-03","arxiv_id":"2308.02046","repositories_listed":1,"syntology":null},{"url":"/paper/gapro-box-supervised-3d-point-cloud-instance","slug":"gapro-box-supervised-3d-point-cloud-instance","title":"GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers","date":"2023-07-25","arxiv_id":"2307.13251","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gapro-box-supervised-3d-point-cloud-instance#ran","syntology_url":"https://syntology.ai/paper/2307.13251","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13251"}},"official":{"repos":["vinairesearch/gapro"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/unmasking-anomalies-in-road-scene","slug":"unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","date":"2023-07-25","arxiv_id":"2307.13316","repositories_listed":1,"syntology":null},{"url":"/paper/ctvis-consistent-training-for-online-video","slug":"ctvis-consistent-training-for-online-video","title":"CTVIS: Consistent Training for Online Video Instance Segmentation","date":"2023-07-24","arxiv_id":"2307.12616","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ctvis-consistent-training-for-online-video#ran","syntology_url":"https://syntology.ai/paper/2307.12616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12616"}},"official":{"repos":["kainingying/ctvis"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dq-det-learning-dynamic-query-combinations","slug":"dq-det-learning-dynamic-query-combinations","title":"Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation","date":"2023-07-23","arxiv_id":"2307.12239","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dq-det-learning-dynamic-query-combinations#ran","syntology_url":"https://syntology.ai/paper/2307.12239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12239"}},"official":{"repos":["bytedance/dq-det"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/calibnet-dual-branch-cross-modal-calibration","slug":"calibnet-dual-branch-cross-modal-calibration","title":"CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation","date":"2023-07-16","arxiv_id":"2307.08098","repositories_listed":1,"syntology":null},{"url":"/paper/syntable-a-synthetic-data-generation-pipeline","slug":"syntable-a-synthetic-data-generation-pipeline","title":"SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes","date":"2023-07-14","arxiv_id":"2307.07333","repositories_listed":1,"syntology":null},{"url":"/paper/anystar-domain-randomized-universal-star","slug":"anystar-domain-randomized-universal-star","title":"AnyStar: Domain randomized universal star-convex 3D instance segmentation","date":"2023-07-13","arxiv_id":"2307.07044","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/anystar-domain-randomized-universal-star#ran","syntology_url":"https://syntology.ai/paper/2307.07044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07044"}},"official":{"repos":["neel-dey/anystar"],"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/sam-path-a-segment-anything-model-for","slug":"sam-path-a-segment-anything-model-for","title":"SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology","date":"2023-07-12","arxiv_id":"2307.09570","repositories_listed":1,"syntology":null},{"url":"/paper/towards-accurate-instance-segmentation-in","slug":"towards-accurate-instance-segmentation-in","title":"Towards accurate instance segmentation in large-scale LiDAR point clouds","date":"2023-07-06","arxiv_id":"2307.02877","repositories_listed":1,"syntology":null},{"url":"/paper/effseg-efficient-fine-grained-instance","slug":"effseg-efficient-fine-grained-instance","title":"EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving Sparsity","date":"2023-07-04","arxiv_id":"2307.01545","repositories_listed":1,"syntology":null},{"url":"/paper/surgical-fine-tuning-for-grape-bunch","slug":"surgical-fine-tuning-for-grape-bunch","title":"Surgical fine-tuning for Grape Bunch Segmentation under Visual Domain Shifts","date":"2023-07-03","arxiv_id":"2307.00837","repositories_listed":1,"syntology":null},{"url":"/paper/mobilevig-graph-based-sparse-attention-for","slug":"mobilevig-graph-based-sparse-attention-for","title":"MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications","date":"2023-07-01","arxiv_id":"2307.00395","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/mobilevig-graph-based-sparse-attention-for#ran","syntology_url":"https://syntology.ai/paper/2307.00395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00395"}},"official":{"repos":["sldgroup/mobilevig"],"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/insta-beeer-explicit-error-estimation-and","slug":"insta-beeer-explicit-error-estimation-and","title":"High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement","date":"2023-06-28","arxiv_id":"2306.16132","repositories_listed":1,"syntology":null},{"url":"/paper/rsprompter-learning-to-prompt-for-remote","slug":"rsprompter-learning-to-prompt-for-remote","title":"RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model","date":"2023-06-28","arxiv_id":"2306.16269","repositories_listed":1,"syntology":null},{"url":"/paper/panet-lidar-panoptic-segmentation-with-sparse","slug":"panet-lidar-panoptic-segmentation-with-sparse","title":"PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation","date":"2023-06-27","arxiv_id":"2306.15348","repositories_listed":1,"syntology":null},{"url":"/paper/the-second-place-solution-for-cvpr-vision-23","slug":"the-second-place-solution-for-cvpr-vision-23","title":"The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection","date":"2023-06-25","arxiv_id":"2306.14116","repositories_listed":1,"syntology":null}],"record_sha256":"4d1edcaa2f65242365cc3371a7038bc8f334d0f3b7d1e117cb941b471f388d40","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}