{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/segmentation/papers/56","list_of":"/task/segmentation","task":"Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":56,"pages_in_order":131,"rows_per_page":100,"rows":[5501,5600],"of":13072,"counts":{"archive_papers_tagged":13072,"with_a_code_link":5255,"where_syntology_ran_a_sample":976,"not_listed_spam_title":0,"listed":13072,"listed_where_code_ran":976,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":838,"every_run_a_failure_of_syntologys_instrument":138,"listed_with_a_run_with_no_instrument_failure":838,"listed_every_run_a_failure_of_syntologys_instrument":138,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/segmentation","prev":"/task/segmentation/papers/55","next":"/task/segmentation/papers/57","papers":[{"url":null,"slug":"flairbrainseg-fine-grained-brain-segmentation","title":"FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only","date":"2025-04-04","arxiv_id":"2504.03376","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-encoder-nnu-net-outperforms-transformer","title":"Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining","date":"2025-04-04","arxiv_id":"2504.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-granularity-vision-fastformer-with","title":"Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation","date":"2025-04-04","arxiv_id":"2504.03108","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-human-registration-with-body-part","title":"Robust Human Registration with Body Part Segmentation on Noisy Point Clouds","date":"2025-04-04","arxiv_id":"2504.03602","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmark-of-segmentation-techniques-for","title":"Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge","date":"2025-04-03","arxiv_id":"2504.02382","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-and-enhancing-segmentation-model","title":"Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing","date":"2025-04-03","arxiv_id":"2504.02335","repositories_listed":0,"syntology":null},{"url":null,"slug":"marine-saliency-segmenter-object-focused","title":"Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation","date":"2025-04-03","arxiv_id":"2504.02391","repositories_listed":0,"syntology":null},{"url":null,"slug":"selfmedhpm-self-pre-training-with-hard","title":"SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation","date":"2025-04-03","arxiv_id":"2504.02524","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizing-temporal-action","title":"Towards Generalizing Temporal Action Segmentation to Unseen Views","date":"2025-04-03","arxiv_id":"2504.02512","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-topology-preserving-three-stage-framework","title":"A topology-preserving three-stage framework for fully-connected coronary artery extraction","date":"2025-04-02","arxiv_id":"2504.01597","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-the-spatial-robustness-of-dnns","title":"Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions","date":"2025-04-02","arxiv_id":"2504.01632","repositories_listed":0,"syntology":null},{"url":null,"slug":"biseg-sam-weakly-supervised-post-processing","title":"BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models","date":"2025-04-02","arxiv_id":"2504.01452","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-migration-diffusion-for-nuclear","title":"Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology","date":"2025-04-02","arxiv_id":"2504.01577","repositories_listed":0,"syntology":null},{"url":null,"slug":"protoguard-guided-propel-class-aware","title":"ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation","date":"2025-04-02","arxiv_id":"2504.01648","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-variability-and-radiomics","title":"Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging","date":"2025-04-02","arxiv_id":"2504.01692","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-foundation-medical","title":"Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates","date":"2025-04-02","arxiv_id":"2504.02008","repositories_listed":0,"syntology":null},{"url":null,"slug":"ripvis-rip-currents-video-instance","title":"RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety","date":"2025-04-01","arxiv_id":"2504.01128","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-4d-lidar-panoptic-segmentation","title":"Zero-Shot 4D Lidar Panoptic Segmentation","date":"2025-04-01","arxiv_id":"2504.00848","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-vision-foundation-models-for-real","title":"Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation","date":"2025-03-31","arxiv_id":"2503.24368","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossformer-cross-segment-semantic-fusion-for","title":"CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation","date":"2025-03-31","arxiv_id":"2503.23671","repositories_listed":0,"syntology":null},{"url":null,"slug":"polypsegtrack-unified-foundation-model-for","title":"PolypSegTrack: Unified Foundation Model for Colonoscopy Video Analysis","date":"2025-03-31","arxiv_id":"2503.24108","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-with-3d-synthetic-data-learning","title":"Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes","date":"2025-03-31","arxiv_id":"2503.24229","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-pretraining-for-aerial-road","title":"Self-Supervised Pretraining for Aerial Road Extraction","date":"2025-03-31","arxiv_id":"2503.24326","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-self-supervised-learning-for-one","title":"Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation","date":"2025-03-30","arxiv_id":"2503.23507","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-vocabulary-semantic-segmentation-with-3","title":"Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments","date":"2025-03-29","arxiv_id":"2503.23105","repositories_listed":0,"syntology":null},{"url":null,"slug":"parsing-through-boundaries-in-chinese-word","title":"Parsing Through Boundaries in Chinese Word Segmentation","date":"2025-03-29","arxiv_id":"2503.23091","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dataset-for-semantic-segmentation-in-the","title":"A Dataset for Semantic Segmentation in the Presence of Unknowns","date":"2025-03-28","arxiv_id":"2503.22309","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-framework-for-boundary-aware","title":"A Deep Learning Framework for Boundary-Aware Semantic Segmentation","date":"2025-03-28","arxiv_id":"2503.22050","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-distance-based-metric-for-quality","title":"A Novel Distance-Based Metric for Quality Assessment in Image Segmentation","date":"2025-03-28","arxiv_id":"2504.00023","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-foundation-models-for-sea-ice-type","title":"Assessing Foundation Models for Sea Ice Type Segmentation in Sentinel-1 SAR Imagery","date":"2025-03-28","arxiv_id":"2503.22516","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-aware-lora-for-domain-aligned","title":"Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation","date":"2025-03-28","arxiv_id":"2503.22172","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-epistemic-uncertainty-estimation-in","title":"Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation","date":"2025-03-28","arxiv_id":"2503.22271","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-deeplabv3-to-fuse-aerial-and","title":"Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation","date":"2025-03-28","arxiv_id":"2503.22909","repositories_listed":0,"syntology":null},{"url":null,"slug":"kevs-enhancing-segmentation-of-visceral","title":"KEVS: Enhancing Segmentation of Visceral Adipose Tissue in Pre-Cystectomy CT with Gaussian Kernel Density Estimation","date":"2025-03-28","arxiv_id":"2503.22592","repositories_listed":0,"syntology":null},{"url":null,"slug":"schnet-sam-marries-clip-for-human-parsing","title":"SCHNet: SAM Marries CLIP for Human Parsing","date":"2025-03-28","arxiv_id":"2503.22237","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-any-motion-in-videos","title":"Segment Any Motion in Videos","date":"2025-03-28","arxiv_id":"2503.22268","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-then-splat-a-unified-approach-for-3d","title":"Segment then Splat: A Unified Approach for 3D Open-Vocabulary Segmentation based on Gaussian Splatting","date":"2025-03-28","arxiv_id":"2503.22204","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-domain-generalization-of","title":"Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study","date":"2025-03-28","arxiv_id":"2503.22862","repositories_listed":0,"syntology":null},{"url":null,"slug":"ama-sam-adversarial-multi-domain-alignment-of","title":"AMA-SAM: Adversarial Multi-Domain Alignment of Segment Anything Model for High-Fidelity Histology Nuclei Segmentation","date":"2025-03-27","arxiv_id":"2503.21695","repositories_listed":0,"syntology":null},{"url":null,"slug":"ducksegmentation-a-segmentation-model-based","title":"DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset","date":"2025-03-27","arxiv_id":"2503.21323","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-devil-is-in-low-level-features-for-cross","title":"The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation","date":"2025-03-27","arxiv_id":"2503.21150","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-robustness-of-cortical-morphometry","title":"Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling","date":"2025-03-26","arxiv_id":"2503.20571","repositories_listed":0,"syntology":null},{"url":null,"slug":"flip-learning-weakly-supervised-erase-to","title":"Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound","date":"2025-03-26","arxiv_id":"2503.20685","repositories_listed":0,"syntology":null},{"url":null,"slug":"medsegnet10-a-publicly-accessible-network","title":"MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation","date":"2025-03-26","arxiv_id":"2503.20830","repositories_listed":0,"syntology":null},{"url":null,"slug":"biprompt-sam-enhancing-image-segmentation-via","title":"BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts","date":"2025-03-25","arxiv_id":"2503.19769","repositories_listed":0,"syntology":null},{"url":null,"slug":"grn-a-simplified-generative-reinforcement","title":"GRN+: A Simplified Generative Reinforcement Network for Tissue Layer Analysis in 3D Ultrasound Images for Chronic Low-back Pain","date":"2025-03-25","arxiv_id":"2503.19736","repositories_listed":0,"syntology":null},{"url":null,"slug":"intersliceboost-identifying-tissue-layers-in","title":"InterSliceBoost: Identifying Tissue Layers in Three-dimensional Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment","date":"2025-03-25","arxiv_id":"2503.19735","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-medsam-model-based-on","title":"Optimization of MedSAM model based on bounding box adaptive perturbation algorithm","date":"2025-03-25","arxiv_id":"2503.19700","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-and-segment-universal-medical-image","title":"Show and Segment: Universal Medical Image Segmentation via In-Context Learning","date":"2025-03-25","arxiv_id":"2503.19359","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-or-tell-effectively-prompting-vision","title":"Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation","date":"2025-03-25","arxiv_id":"2503.19647","repositories_listed":0,"syntology":null},{"url":null,"slug":"egosurgery-hts-a-dataset-for-egocentric-hand","title":"EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery Videos","date":"2025-03-24","arxiv_id":"2503.18755","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-model-for-whole-heart-segmentation","title":"Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging","date":"2025-03-24","arxiv_id":"2503.19005","repositories_listed":0,"syntology":null},{"url":null,"slug":"pddm-pseudo-depth-diffusion-model-for-rgb-pd","title":"PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes","date":"2025-03-24","arxiv_id":"2503.18393","repositories_listed":0,"syntology":null},{"url":null,"slug":"tuning-free-amodal-segmentation-via-the","title":"Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models","date":"2025-03-24","arxiv_id":"2503.18947","repositories_listed":0,"syntology":null},{"url":null,"slug":"mllm-for3d-adapting-multimodal-large-language","title":"MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation","date":"2025-03-23","arxiv_id":"2503.18135","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-disease-aware-training-strategy-for","title":"Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation","date":"2025-03-23","arxiv_id":"2503.17896","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attention-diffusion-models-for-zero-shot","title":"Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging","date":"2025-03-23","arxiv_id":"2503.18170","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-preserving-image-segmentation-using","title":"Topology preserving Image segmentation using the iterative convolution-thresholding method","date":"2025-03-22","arxiv_id":"2503.17792","repositories_listed":0,"syntology":null},{"url":null,"slug":"downstream-analysis-of-foundational-medical","title":"Downstream Analysis of Foundational Medical Vision Models for Disease Progression","date":"2025-03-21","arxiv_id":"2503.16842","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-accuracy-pulmonary-vessel-segmentation","title":"High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation","date":"2025-03-21","arxiv_id":"2503.16988","repositories_listed":0,"syntology":null},{"url":null,"slug":"mm-unet-meta-mamba-unet-for-medical-image","title":"MM-UNet: Meta Mamba UNet for Medical Image Segmentation","date":"2025-03-21","arxiv_id":"2503.17540","repositories_listed":0,"syntology":null},{"url":null,"slug":"closer-to-ground-truth-realistic-shape-and","title":"Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation","date":"2025-03-20","arxiv_id":"2503.16051","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-transparent-and","title":"Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning","date":"2025-03-19","arxiv_id":"2503.15004","repositories_listed":0,"syntology":null},{"url":null,"slug":"semanticflow-a-self-supervised-framework-for","title":"SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments","date":"2025-03-19","arxiv_id":"2503.14837","repositories_listed":0,"syntology":null},{"url":null,"slug":"spnerf-open-vocabulary-3d-neural-scene","title":"SPNeRF: Open Vocabulary 3D Neural Scene Segmentation with Superpoints","date":"2025-03-19","arxiv_id":"2503.15712","repositories_listed":0,"syntology":null},{"url":null,"slug":"sum-parts-benchmarking-part-level-semantic","title":"SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes","date":"2025-03-19","arxiv_id":"2503.15300","repositories_listed":0,"syntology":null},{"url":null,"slug":"usam-net-a-u-net-based-network-for-improved","title":"USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network","date":"2025-03-19","arxiv_id":"2503.14950","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-revisit-to-the-decoder-for-camouflaged","title":"A Revisit to the Decoder for Camouflaged Object Detection","date":"2025-03-18","arxiv_id":"2503.14035","repositories_listed":0,"syntology":null},{"url":null,"slug":"mast-pro-dynamic-mixture-of-experts-for","title":"MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts","date":"2025-03-18","arxiv_id":"2503.14355","repositories_listed":0,"syntology":null},{"url":null,"slug":"organ-aware-multi-scale-medical-image","title":"Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering","date":"2025-03-18","arxiv_id":"2503.13806","repositories_listed":0,"syntology":null},{"url":"/paper/psa-ssl-pose-and-size-aware-self-supervised","slug":"psa-ssl-pose-and-size-aware-self-supervised","title":"PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds","date":"2025-03-18","arxiv_id":"2503.13914","repositories_listed":0,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/psa-ssl-pose-and-size-aware-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2503.13914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13914"}},"official":null}},{"url":null,"slug":"romedformer-a-rotary-embedding-transformer","title":"RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT","date":"2025-03-18","arxiv_id":"2503.14304","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam2-for-image-and-video-segmentation-a","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","date":"2025-03-17","arxiv_id":"2503.12781","repositories_listed":0,"syntology":null},{"url":null,"slug":"sed-mvs-segmentation-driven-and-edge-aligned","title":"SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint","date":"2025-03-17","arxiv_id":"2503.13721","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-powered-automated-model-construction-for","title":"AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows","date":"2025-03-16","arxiv_id":"2503.12515","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-based-scene-segmentation-a-survey","title":"Point Cloud Based Scene Segmentation: A Survey","date":"2025-03-16","arxiv_id":"2503.12595","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-any-quality-images-with-generative","title":"Segment Any-Quality Images with Generative Latent Space Enhancement","date":"2025-03-16","arxiv_id":"2503.12507","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-sam-training-free-segment-every-entity","title":"E-SAM: Training-Free Segment Every Entity Model","date":"2025-03-15","arxiv_id":"2503.12094","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyclepose-leveraging-cycle-consistency-for","title":"CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy","date":"2025-03-14","arxiv_id":"2503.11266","repositories_listed":0,"syntology":null},{"url":null,"slug":"egosplat-open-vocabulary-egocentric-scene","title":"EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting","date":"2025-03-14","arxiv_id":"2503.11345","repositories_listed":0,"syntology":null},{"url":null,"slug":"spaceseg-a-high-precision-intelligent","title":"SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets","date":"2025-03-14","arxiv_id":"2503.11133","repositories_listed":0,"syntology":null},{"url":null,"slug":"category-prompt-mamba-network-for-nuclei","title":"Category Prompt Mamba Network for Nuclei Segmentation and Classification","date":"2025-03-13","arxiv_id":"2503.10422","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-automated-workflow-for","title":"Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans","date":"2025-03-13","arxiv_id":"2503.10717","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-direct-leaf-area","title":"Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development","date":"2025-03-13","arxiv_id":"2503.10129","repositories_listed":0,"syntology":null},{"url":null,"slug":"eye-on-the-target-eye-tracking-meets-rodent","title":"Eye on the Target: Eye Tracking Meets Rodent Tracking","date":"2025-03-13","arxiv_id":"2503.10305","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-one-a-single-example-is-all-it","title":"The Power of One: A Single Example is All it Takes for Segmentation in VLMs","date":"2025-03-13","arxiv_id":"2503.10779","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-the-invisible-reasoning-complex-1","title":"Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA","date":"2025-03-13","arxiv_id":"2503.10225","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-context-to-improve-word-segmentation","title":"Using Context to Improve Word Segmentation","date":"2025-03-13","arxiv_id":"2503.10023","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-state-of-the-art-deep-learning","title":"Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms","date":"2025-03-12","arxiv_id":"2503.08970","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigation-of-frame-differences-as-motion","title":"Investigation of Frame Differences as Motion Cues for Video Object Segmentation","date":"2025-03-12","arxiv_id":"2503.09132","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-consultation-for-semi-supervised","title":"Knowledge Consultation for Semi-Supervised Semantic Segmentation","date":"2025-03-12","arxiv_id":"2503.10693","repositories_listed":0,"syntology":null},{"url":null,"slug":"mono2d-a-trainable-monogenic-layer-for-robust","title":"Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data","date":"2025-03-12","arxiv_id":"2503.09050","repositories_listed":0,"syntology":null},{"url":null,"slug":"surgicalvlm-agent-towards-an-interactive-ai","title":"SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery","date":"2025-03-12","arxiv_id":"2503.09474","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-medical-imaging-segmentation-on-non","title":"3D Medical Imaging Segmentation on Non-Contrast CT","date":"2025-03-11","arxiv_id":"2503.08361","repositories_listed":0,"syntology":null},{"url":null,"slug":"maskattn-unet-a-mask-attention-driven","title":"MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation","date":"2025-03-11","arxiv_id":"2503.10686","repositories_listed":0,"syntology":null},{"url":null,"slug":"rel-unet-reliable-tumor-segmentation-via","title":"Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net","date":"2025-03-11","arxiv_id":"2503.09633","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-size-targets-are-sufficient-for","title":"Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation","date":"2025-03-10","arxiv_id":"2503.06954","repositories_listed":0,"syntology":null},{"url":null,"slug":"customized-sam-2-for-referring-remote-sensing","title":"Customized SAM 2 for Referring Remote Sensing Image Segmentation","date":"2025-03-10","arxiv_id":"2503.07266","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantu-net-efficient-wearable-medical-imaging","title":"QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter","date":"2025-03-10","arxiv_id":"2503.08719","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-specific-knowledge-distillation-from-the","title":"Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation","date":"2025-03-10","arxiv_id":"2503.06976","repositories_listed":0,"syntology":null}],"record_sha256":"6f08bbf337d8aa87cf9a3e8536247b8ae507162ce2120bdb35b289622d0aace7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}