{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/semantic-segmentation/papers/15","list_of":"/task/semantic-segmentation","task":"Semantic Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":15,"pages_in_order":148,"rows_per_page":100,"rows":[1401,1500],"of":14763,"counts":{"archive_papers_tagged":14763,"with_a_code_link":6644,"where_syntology_ran_a_sample":1583,"not_listed_spam_title":0,"listed":14763,"listed_where_code_ran":1583,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1384,"every_run_a_failure_of_syntologys_instrument":199,"listed_with_a_run_with_no_instrument_failure":1384,"listed_every_run_a_failure_of_syntologys_instrument":199,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/semantic-segmentation","prev":"/task/semantic-segmentation/papers/14","next":"/task/semantic-segmentation/papers/16","papers":[{"url":"/paper/sama-unet-enhancing-medical-image","slug":"sama-unet-enhancing-medical-image","title":"SAMA-UNet: Enhancing Medical Image Segmentation with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning","date":"2025-05-21","arxiv_id":"2505.15234","repositories_listed":1,"syntology":null},{"url":"/paper/seg-3d-by-pc2d-multi-view-projection-for","slug":"seg-3d-by-pc2d-multi-view-projection-for","title":"seg_3D_by_PC2D: Multi-View Projection for Domain Generalization and Adaptation in 3D Semantic Segmentation","date":"2025-05-21","arxiv_id":"2505.15545","repositories_listed":1,"syntology":null},{"url":"/paper/uwsam-segment-anything-model-guided","slug":"uwsam-segment-anything-model-guided","title":"UWSAM: Segment Anything Model Guided Underwater Instance Segmentation and A Large-scale Benchmark Dataset","date":"2025-05-21","arxiv_id":"2505.15581","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-classifier-for-boosting-few-shot-1","slug":"decoupling-classifier-for-boosting-few-shot-1","title":"Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation","date":"2025-05-20","arxiv_id":"2505.14239","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/decoupling-classifier-for-boosting-few-shot-1#ran","syntology_url":"https://syntology.ai/paper/2505.14239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.14239"}},"official":null}},{"url":"/paper/intra-class-patch-swap-for-self-distillation","slug":"intra-class-patch-swap-for-self-distillation","title":"Intra-class Patch Swap for Self-Distillation","date":"2025-05-20","arxiv_id":"2505.14124","repositories_listed":1,"syntology":null},{"url":"/paper/lod1-3d-city-model-from-lidar-the-impact-of","slug":"lod1-3d-city-model-from-lidar-the-impact-of","title":"LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology Extraction","date":"2025-05-20","arxiv_id":"2505.14747","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multimodal-segmentation-with","slug":"robust-multimodal-segmentation-with","title":"Robust Multimodal Segmentation with Representation Regularization and Hybrid Prototype Distillation","date":"2025-05-19","arxiv_id":"2505.12861","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-spectral-spatial-unified-remote","slug":"temporal-spectral-spatial-unified-remote","title":"Temporal-Spectral-Spatial Unified Remote Sensing Dense Prediction","date":"2025-05-18","arxiv_id":"2505.12280","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-inter-domain-gap-through-low","slug":"bridging-the-inter-domain-gap-through-low","title":"Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation","date":"2025-05-17","arxiv_id":"2505.11909","repositories_listed":1,"syntology":null},{"url":"/paper/medvkan-efficient-feature-extraction-with","slug":"medvkan-efficient-feature-extraction-with","title":"MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation","date":"2025-05-17","arxiv_id":"2505.11797","repositories_listed":1,"syntology":null},{"url":"/paper/softpq-robust-instance-segmentation","slug":"softpq-robust-instance-segmentation","title":"SoftPQ: Robust Instance Segmentation Evaluation via Soft Matching and Tunable Thresholds","date":"2025-05-17","arxiv_id":"2505.12155","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11157","slug":"2505-11157","title":"Attention on the Sphere","date":"2025-05-16","arxiv_id":"2505.11157","repositories_listed":1,"syntology":null},{"url":"/paper/2505-10751","slug":"2505-10751","title":"Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis","date":"2025-05-15","arxiv_id":"2505.10751","repositories_listed":1,"syntology":null},{"url":"/paper/apcotta-continual-test-time-adaptation-for","slug":"apcotta-continual-test-time-adaptation-for","title":"APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds","date":"2025-05-15","arxiv_id":"2505.09971","repositories_listed":1,"syntology":null},{"url":"/paper/data-agnostic-augmentations-for-unknown","slug":"data-agnostic-augmentations-for-unknown","title":"Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation","date":"2025-05-15","arxiv_id":"2505.10223","repositories_listed":1,"syntology":null},{"url":"/paper/ddfp-data-dependent-frequency-prompt-for","slug":"ddfp-data-dependent-frequency-prompt-for","title":"DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation","date":"2025-05-15","arxiv_id":"2505.09927","repositories_listed":1,"syntology":null},{"url":"/paper/hwa-unetr-hierarchical-window-aggregate-unetr","slug":"hwa-unetr-hierarchical-window-aggregate-unetr","title":"HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation","date":"2025-05-15","arxiv_id":"2505.10464","repositories_listed":1,"syntology":null},{"url":"/paper/spikevideoformer-an-efficient-spike-driven","slug":"spikevideoformer-an-efficient-spike-driven","title":"SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\\mathcal{O}(T)$ Complexity","date":"2025-05-15","arxiv_id":"2505.10352","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spikevideoformer-an-efficient-spike-driven#ran","syntology_url":"https://syntology.ai/paper/2505.10352","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.10352"}},"official":{"repos":["jimmyzou/spikevideoformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/metauas-universal-anomaly-segmentation-with","slug":"metauas-universal-anomaly-segmentation-with","title":"MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning","date":"2025-05-14","arxiv_id":"2505.09265","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/metauas-universal-anomaly-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2505.09265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.09265"}},"official":null}},{"url":"/paper/recent-advances-in-medical-imaging","slug":"recent-advances-in-medical-imaging","title":"Recent Advances in Medical Imaging Segmentation: A Survey","date":"2025-05-14","arxiv_id":"2505.09274","repositories_listed":1,"syntology":null},{"url":"/paper/skull-stripping-with-purely-synthetic-data","slug":"skull-stripping-with-purely-synthetic-data","title":"Skull stripping with purely synthetic data","date":"2025-05-12","arxiv_id":"2505.07159","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-guided-diffusion-model-for-single","slug":"semantic-guided-diffusion-model-for-single","title":"Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution","date":"2025-05-11","arxiv_id":"2505.07071","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/semantic-guided-diffusion-model-for-single#ran","syntology_url":"https://syntology.ai/paper/2505.07071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.07071"}},"official":{"repos":["liu-zihang/samsr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dfen-dual-feature-equalization-network-for","slug":"dfen-dual-feature-equalization-network-for","title":"DFEN: Dual Feature Equalization Network for Medical Image Segmentation","date":"2025-05-09","arxiv_id":"2505.05913","repositories_listed":1,"syntology":null},{"url":"/paper/noise-consistent-siamese-diffusion-for","slug":"noise-consistent-siamese-diffusion-for","title":"Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation","date":"2025-05-09","arxiv_id":"2505.06068","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/noise-consistent-siamese-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2505.06068","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.06068"}},"official":{"repos":["qiukunpeng/siamese-diffusion"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainsam-fast-and-efficient-uncertainty","slug":"uncertainsam-fast-and-efficient-uncertainty","title":"UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model","date":"2025-05-08","arxiv_id":"2505.05049","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uncertainsam-fast-and-efficient-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2505.05049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.05049"}},"official":{"repos":["GreenAutoML4FAS/UncertainSAM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/are-synthetic-corruptions-a-reliable-proxy","slug":"are-synthetic-corruptions-a-reliable-proxy","title":"Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?","date":"2025-05-07","arxiv_id":"2505.04835","repositories_listed":1,"syntology":null},{"url":"/paper/declip-decoupled-learning-for-open-vocabulary","slug":"declip-decoupled-learning-for-open-vocabulary","title":"DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception","date":"2025-05-07","arxiv_id":"2505.04410","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/declip-decoupled-learning-for-open-vocabulary#ran","syntology_url":"https://syntology.ai/paper/2505.04410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.04410"}},"official":{"repos":["xiaomoguhz/declip"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/panoramic-out-of-distribution-segmentation","slug":"panoramic-out-of-distribution-segmentation","title":"Panoramic Out-of-Distribution Segmentation","date":"2025-05-06","arxiv_id":"2505.03539","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-boundary-detection-in-deep-1","slug":"rethinking-boundary-detection-in-deep-1","title":"Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation","date":"2025-05-06","arxiv_id":"2505.04652","repositories_listed":1,"syntology":null},{"url":"/paper/show-or-tell-a-benchmark-to-evaluate-visual","slug":"show-or-tell-a-benchmark-to-evaluate-visual","title":"Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation","date":"2025-05-06","arxiv_id":"2505.06280","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-robustness-analysis-of-vision","slug":"adversarial-robustness-analysis-of-vision","title":"Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation","date":"2025-05-05","arxiv_id":"2505.02971","repositories_listed":1,"syntology":null},{"url":"/paper/intellicardiac-an-intelligent-platform-for","slug":"intellicardiac-an-intelligent-platform-for","title":"IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification","date":"2025-05-05","arxiv_id":"2505.03838","repositories_listed":1,"syntology":null},{"url":"/paper/a-sensor-agnostic-domain-generalization","slug":"a-sensor-agnostic-domain-generalization","title":"A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning","date":"2025-05-02","arxiv_id":"2505.01558","repositories_listed":1,"syntology":null},{"url":"/paper/core-set-selection-for-data-efficient-land","slug":"core-set-selection-for-data-efficient-land","title":"Core-Set Selection for Data-efficient Land Cover Segmentation","date":"2025-05-02","arxiv_id":"2505.01225","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-rgb-event-semantic-segmentation","slug":"rethinking-rgb-event-semantic-segmentation","title":"Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation","date":"2025-05-02","arxiv_id":"2505.01548","repositories_listed":1,"syntology":null},{"url":"/paper/vision-mamba-in-remote-sensing-a","slug":"vision-mamba-in-remote-sensing-a","title":"Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook","date":"2025-05-01","arxiv_id":"2505.00630","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-semantic-segmentation-of-high","slug":"real-time-semantic-segmentation-of-high","title":"Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans","date":"2025-04-30","arxiv_id":"2504.21602","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-context-learning-of-object","slug":"hierarchical-context-learning-of-object","title":"Hierarchical Context Learning of object components for unsupervised semantic segmentation","date":"2025-04-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/og-hfyolo-orientation-gradient-guidance-and","slug":"og-hfyolo-orientation-gradient-guidance-and","title":"OG-HFYOLO :Orientation gradient guidance and heterogeneous feature fusion for deformation table cell instance segmentation","date":"2025-04-29","arxiv_id":"2504.20682","repositories_listed":1,"syntology":null},{"url":"/paper/magnifier-a-multi-grained-neural-network","slug":"magnifier-a-multi-grained-neural-network","title":"Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation","date":"2025-04-28","arxiv_id":"2504.19589","repositories_listed":1,"syntology":null},{"url":"/paper/what-is-the-added-value-of-uda-in-the-vfm-era","slug":"what-is-the-added-value-of-uda-in-the-vfm-era","title":"What is the Added Value of UDA in the VFM Era?","date":"2025-04-25","arxiv_id":"2504.18190","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-sea-a-mamba-based-framework-with-global","slug":"mamba-sea-a-mamba-based-framework-with-global","title":"Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation","date":"2025-04-24","arxiv_id":"2504.17515","repositories_listed":1,"syntology":null},{"url":"/paper/semanticsugarbeets-a-multi-task-framework-and-1","slug":"semanticsugarbeets-a-multi-task-framework-and-1","title":"SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets","date":"2025-04-23","arxiv_id":"2504.16684","repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-with-noisy-labels-via-spatially","slug":"segmentation-with-noisy-labels-via-spatially","title":"Segmentation with Noisy Labels via Spatially Correlated Distributions","date":"2025-04-21","arxiv_id":"2504.14795","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-referring-video-single-and-multi","slug":"few-shot-referring-video-single-and-multi","title":"Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence Matching","date":"2025-04-18","arxiv_id":"2504.13710","repositories_listed":1,"syntology":null},{"url":"/paper/hdbformer-efficient-rgb-d-semantic","slug":"hdbformer-efficient-rgb-d-semantic","title":"HDBFormer: Efficient RGB-D Semantic Segmentation with A Heterogeneous Dual-Branch Framework","date":"2025-04-18","arxiv_id":"2504.13579","repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-noisy-pseudo-labels-for-all","slug":"learning-from-noisy-pseudo-labels-for-all","title":"Learning from Noisy Pseudo-labels for All-Weather Land Cover Mapping","date":"2025-04-18","arxiv_id":"2504.13458","repositories_listed":1,"syntology":null},{"url":"/paper/digital-twin-generation-from-visual-data-a","slug":"digital-twin-generation-from-visual-data-a","title":"Digital Twin Generation from Visual Data: A Survey","date":"2025-04-17","arxiv_id":"2504.13159","repositories_listed":1,"syntology":null},{"url":"/paper/putting-the-segment-anything-model-to-the","slug":"putting-the-segment-anything-model-to-the","title":"Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance","date":"2025-04-17","arxiv_id":"2504.13340","repositories_listed":1,"syntology":null},{"url":"/paper/stronger-steadier-superior-geometric","slug":"stronger-steadier-superior-geometric","title":"Stronger, Steadier & Superior: Geometric Consistency in Depth VFM Forges Domain Generalized Semantic Segmentation","date":"2025-04-17","arxiv_id":"2504.12753","repositories_listed":1,"syntology":null},{"url":"/paper/3d-pointzshots-geometry-aware-3d-point-cloud","slug":"3d-pointzshots-geometry-aware-3d-point-cloud","title":"3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap","date":"2025-04-16","arxiv_id":"2504.12442","repositories_listed":1,"syntology":null},{"url":"/paper/dc-sam-in-context-segment-anything-in-images","slug":"dc-sam-in-context-segment-anything-in-images","title":"DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency","date":"2025-04-16","arxiv_id":"2504.12080","repositories_listed":1,"syntology":null},{"url":"/paper/grabs-generative-embodied-agent-for-3d-object","slug":"grabs-generative-embodied-agent-for-3d-object","title":"GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision","date":"2025-04-16","arxiv_id":"2504.11754","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/grabs-generative-embodied-agent-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/2504.11754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.11754"}},"official":{"repos":["vlar-group/grabs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aligning-generative-denoising-with","slug":"aligning-generative-denoising-with","title":"Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception","date":"2025-04-15","arxiv_id":"2504.11457","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/aligning-generative-denoising-with#ran","syntology_url":"https://syntology.ai/paper/2504.11457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.11457"}},"official":{"repos":["ziqipang/addp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pranet-v2-dual-supervised-reverse-attention","slug":"pranet-v2-dual-supervised-reverse-attention","title":"PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation","date":"2025-04-15","arxiv_id":"2504.10986","repositories_listed":1,"syntology":null},{"url":"/paper/floss-free-lunch-in-open-vocabulary-semantic","slug":"floss-free-lunch-in-open-vocabulary-semantic","title":"FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation","date":"2025-04-14","arxiv_id":"2504.10487","repositories_listed":1,"syntology":null},{"url":"/paper/m2s-road-multi-modal-semantic-segmentation","slug":"m2s-road-multi-modal-semantic-segmentation","title":"M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data","date":"2025-04-14","arxiv_id":"2504.10123","repositories_listed":1,"syntology":null},{"url":"/paper/masseg-2nd-technical-report-for-4th-pvuw-mose","slug":"masseg-2nd-technical-report-for-4th-pvuw-mose","title":"MASSeg : 2nd Technical Report for 4th PVUW MOSE Track","date":"2025-04-14","arxiv_id":"2504.10254","repositories_listed":1,"syntology":null},{"url":"/paper/the-scalability-of-simplicity-empirical","slug":"the-scalability-of-simplicity-empirical","title":"The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer","date":"2025-04-14","arxiv_id":"2504.10462","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/the-scalability-of-simplicity-empirical#ran","syntology_url":"https://syntology.ai/paper/2504.10462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.10462"}},"official":{"repos":["bytedance/sail"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/a-unified-loss-for-handling-inter-class-and","slug":"a-unified-loss-for-handling-inter-class-and","title":"A Unified Loss for Handling Inter-Class and Intra-Class Imbalance in Medical Image Segmentation","date":"2025-04-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cut-and-splat-leveraging-gaussian-splatting","slug":"cut-and-splat-leveraging-gaussian-splatting","title":"Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation","date":"2025-04-11","arxiv_id":"2504.08473","repositories_listed":1,"syntology":null},{"url":"/paper/sn-lidar-semantic-neural-fields-for-novel","slug":"sn-lidar-semantic-neural-fields-for-novel","title":"SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis","date":"2025-04-11","arxiv_id":"2504.08361","repositories_listed":1,"syntology":null},{"url":"/paper/glus-global-local-reasoning-unified-into-a","slug":"glus-global-local-reasoning-unified-into-a","title":"GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation","date":"2025-04-10","arxiv_id":"2504.07962","repositories_listed":1,"syntology":{"n":12,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":12,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/glus-global-local-reasoning-unified-into-a#ran","syntology_url":"https://syntology.ai/paper/2504.07962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.07962"}},"official":null}},{"url":"/paper/p2object-single-point-supervised-object","slug":"p2object-single-point-supervised-object","title":"P2Object: Single Point Supervised Object Detection and Instance Segmentation","date":"2025-04-10","arxiv_id":"2504.07813","repositories_listed":1,"syntology":null},{"url":"/paper/zs-vcos-zero-shot-outperforms-supervised-1","slug":"zs-vcos-zero-shot-outperforms-supervised-1","title":"ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation","date":"2025-04-10","arxiv_id":"2505.01431","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-through-attenuation-of","slug":"domain-generalization-through-attenuation-of","title":"Domain Generalization through Attenuation of Domain-Specific Information","date":"2025-04-09","arxiv_id":"2504.06781","repositories_listed":1,"syntology":null},{"url":"/paper/wheat3dgs-in-field-3d-reconstruction-instance","slug":"wheat3dgs-in-field-3d-reconstruction-instance","title":"Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting","date":"2025-04-09","arxiv_id":"2504.06978","repositories_listed":1,"syntology":null},{"url":"/paper/earth-adapter-bridge-the-geospatial-domain","slug":"earth-adapter-bridge-the-geospatial-domain","title":"Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation","date":"2025-04-08","arxiv_id":"2504.06220","repositories_listed":1,"syntology":null},{"url":"/paper/hrmedseg-unlocking-high-resolution-medical","slug":"hrmedseg-unlocking-high-resolution-medical","title":"HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling","date":"2025-04-08","arxiv_id":"2504.06205","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-the-nested-u-net-approach","slug":"rethinking-the-nested-u-net-approach","title":"Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion","date":"2025-04-08","arxiv_id":"2504.06158","repositories_listed":1,"syntology":null},{"url":"/paper/towards-varroa-destructor-mite-detection","slug":"towards-varroa-destructor-mite-detection","title":"Towards Varroa destructor mite detection using a narrow spectra illumination","date":"2025-04-08","arxiv_id":"2504.06099","repositories_listed":1,"syntology":null},{"url":"/paper/boxseg-quality-aware-and-peer-assisted","slug":"boxseg-quality-aware-and-peer-assisted","title":"BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation","date":"2025-04-07","arxiv_id":"2504.05137","repositories_listed":1,"syntology":null},{"url":"/paper/caption-anything-in-video-fine-grained-object","slug":"caption-anything-in-video-fine-grained-object","title":"Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting","date":"2025-04-07","arxiv_id":"2504.05541","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/caption-anything-in-video-fine-grained-object#ran","syntology_url":"https://syntology.ai/paper/2504.05541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.05541"}},"official":{"repos":["yunlong10/CAT-V"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dformerv2-geometry-self-attention-for-rgbd","slug":"dformerv2-geometry-self-attention-for-rgbd","title":"DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation","date":"2025-04-07","arxiv_id":"2504.04701","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/dformerv2-geometry-self-attention-for-rgbd#ran","syntology_url":"https://syntology.ai/paper/2504.04701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.04701"}},"official":{"repos":["VCIP-RGBD/DFormer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/nninteractiveslicer-a-3d-slicer-extension-for","slug":"nninteractiveslicer-a-3d-slicer-extension-for","title":"SlicerNNInteractive: A 3D Slicer extension for nnInteractive","date":"2025-04-07","arxiv_id":"2504.07991","repositories_listed":1,"syntology":null},{"url":"/paper/the-1st-solution-for-4th-pvuw-mevis-challenge","slug":"the-1st-solution-for-4th-pvuw-mevis-challenge","title":"The 1st Solution for 4th PVUW MeViS Challenge: Unleashing the Potential of Large Multimodal Models for Referring Video Segmentation","date":"2025-04-07","arxiv_id":"2504.05178","repositories_listed":1,"syntology":null},{"url":"/paper/graphseg-segmented-3d-representations-via","slug":"graphseg-segmented-3d-representations-via","title":"GraphSeg: Segmented 3D Representations via Graph Edge Addition and Contraction","date":"2025-04-04","arxiv_id":"2504.03129","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-as-a-bridge-where-vision-foundation","slug":"mamba-as-a-bridge-where-vision-foundation","title":"Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation","date":"2025-04-04","arxiv_id":"2504.03193","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-frequency-enhancement-network-for-1","slug":"adaptive-frequency-enhancement-network-for-1","title":"Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation","date":"2025-04-03","arxiv_id":"2504.02647","repositories_listed":1,"syntology":null},{"url":"/paper/delineate-anything-resolution-agnostic-field","slug":"delineate-anything-resolution-agnostic-field","title":"Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery","date":"2025-04-03","arxiv_id":"2504.02534","repositories_listed":1,"syntology":null},{"url":"/paper/rip-current-segmentation-a-novel-benchmark","slug":"rip-current-segmentation-a-novel-benchmark","title":"Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results","date":"2025-04-03","arxiv_id":"2504.02558","repositories_listed":1,"syntology":null},{"url":"/paper/scene-centric-unsupervised-panoptic","slug":"scene-centric-unsupervised-panoptic","title":"Scene-Centric Unsupervised Panoptic Segmentation","date":"2025-04-02","arxiv_id":"2504.01955","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/scene-centric-unsupervised-panoptic#ran","syntology_url":"https://syntology.ai/paper/2504.01955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.01955"}},"official":{"repos":["visinf/cups"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-biomedical-image-segmentation","slug":"semi-supervised-biomedical-image-segmentation","title":"Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training","date":"2025-04-02","arxiv_id":"2504.01547","repositories_listed":1,"syntology":null},{"url":"/paper/stpnet-scale-aware-text-prompt-network-for","slug":"stpnet-scale-aware-text-prompt-network-for","title":"STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation","date":"2025-04-02","arxiv_id":"2504.01561","repositories_listed":1,"syntology":null},{"url":"/paper/v-clr-view-consistent-learning-for-open-world","slug":"v-clr-view-consistent-learning-for-open-world","title":"v-CLR: View-Consistent Learning for Open-World Instance Segmentation","date":"2025-04-02","arxiv_id":"2504.01383","repositories_listed":1,"syntology":null},{"url":"/paper/4th-pvuw-mevis-3rd-place-report-sa2va","slug":"4th-pvuw-mevis-3rd-place-report-sa2va","title":"4th PVUW MeViS 3rd Place Report: Sa2VA","date":"2025-04-01","arxiv_id":"2504.00476","repositories_listed":1,"syntology":null},{"url":"/paper/cellvta-enhancing-vision-foundation-models","slug":"cellvta-enhancing-vision-foundation-models","title":"CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification","date":"2025-04-01","arxiv_id":"2504.00784","repositories_listed":1,"syntology":null},{"url":"/paper/deconver-a-deconvolutional-network-for","slug":"deconver-a-deconvolutional-network-for","title":"Deconver: A Deconvolutional Network for Medical Image Segmentation","date":"2025-04-01","arxiv_id":"2504.00302","repositories_listed":1,"syntology":null},{"url":"/paper/fssuwnet-mitigating-the-fragility-of-pre","slug":"fssuwnet-mitigating-the-fragility-of-pre","title":"FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images","date":"2025-04-01","arxiv_id":"2504.00478","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-global-local-representation-with","slug":"hybrid-global-local-representation-with","title":"Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentation","date":"2025-04-01","arxiv_id":"2504.00356","repositories_listed":1,"syntology":null},{"url":"/paper/bipvl-seg-bidirectional-progressive-vision","slug":"bipvl-seg-bidirectional-progressive-vision","title":"BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentation","date":"2025-03-30","arxiv_id":"2503.23534","repositories_listed":1,"syntology":null},{"url":"/paper/improving-underwater-semantic-segmentation","slug":"improving-underwater-semantic-segmentation","title":"Improving underwater semantic segmentation with underwater image quality attention and muti-scale aggregation attention","date":"2025-03-30","arxiv_id":"2503.23422","repositories_listed":1,"syntology":null},{"url":"/paper/referdino-plus-2nd-solution-for-4th-pvuw","slug":"referdino-plus-2nd-solution-for-4th-pvuw","title":"ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025","date":"2025-03-30","arxiv_id":"2503.23509","repositories_listed":1,"syntology":null},{"url":"/paper/zs-vcos-zero-shot-outperforms-supervised","slug":"zs-vcos-zero-shot-outperforms-supervised","title":"ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method","date":"2025-03-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/baseline-systems-and-evaluation-metrics-for","slug":"baseline-systems-and-evaluation-metrics-for","title":"Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes","date":"2025-03-28","arxiv_id":"2503.22088","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-background-shift-rethinking-instance","slug":"beyond-background-shift-rethinking-instance","title":"Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation","date":"2025-03-28","arxiv_id":"2503.22136","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-image-dense-annotation-generation","slug":"a-unified-image-dense-annotation-generation","title":"A Unified Image-Dense Annotation Generation Model for Underwater Scenes","date":"2025-03-27","arxiv_id":"2503.21771","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-unified-image-dense-annotation-generation#ran","syntology_url":"https://syntology.ai/paper/2503.21771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.21771"}},"official":{"repos":["hongklin/tide"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dgsunet-an-improved-unet-model-with-dino","slug":"dgsunet-an-improved-unet-model-with-dino","title":"DSU-Net:An Improved U-Net Model Based on DINOv2 and SAM2 with Multi-scale Cross-model Feature Enhancement","date":"2025-03-27","arxiv_id":"2503.21187","repositories_listed":1,"syntology":null},{"url":"/paper/foveated-instance-segmentation","slug":"foveated-instance-segmentation","title":"Foveated Instance Segmentation","date":"2025-03-27","arxiv_id":"2503.21854","repositories_listed":1,"syntology":null},{"url":"/paper/prompting-vision-language-model-for-nuclei","slug":"prompting-vision-language-model-for-nuclei","title":"Prompting Vision-Language Model for Nuclei Instance Segmentation and Classification","date":"2025-03-27","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"961d914007f23436f76d2e43a221d32abcc75194e40142901a44b5a832d6a1ab","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}