{"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/90","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":90,"pages_in_order":148,"rows_per_page":100,"rows":[8901,9000],"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/89","next":"/task/semantic-segmentation/papers/91","papers":[{"url":null,"slug":"robust-semi-supervised-segmentation-with","title":"Robust semi-supervised segmentation with timestep ensembling diffusion models","date":"2023-11-13","arxiv_id":"2311.07421","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-clutter-detection-and-semantic","title":"Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data","date":"2023-11-13","arxiv_id":"2311.07247","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-based-video-object-segmentation","title":"Sketch-based Video Object Segmentation: Benchmark and Analysis","date":"2023-11-13","arxiv_id":"2311.07261","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-performance-prediction-for-deep","title":"Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks","date":"2023-11-13","arxiv_id":"2311.07477","repositories_listed":0,"syntology":null},{"url":null,"slug":"osteoporosis-prediction-from-hand-and-wrist-x","title":"Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning","date":"2023-11-12","arxiv_id":"2311.06834","repositories_listed":0,"syntology":null},{"url":null,"slug":"3dfusion-a-real-time-3d-object-reconstruction","title":"3DFusion, A real-time 3D object reconstruction pipeline based on streamed instance segmented data","date":"2023-11-11","arxiv_id":"2311.06659","repositories_listed":0,"syntology":null},{"url":null,"slug":"fdnet-feature-decoupled-segmentation-network","title":"FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image","date":"2023-11-11","arxiv_id":"2311.06551","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagonal-hierarchical-consistency-learning","title":"Diagonal Hierarchical Consistency Learning for Semi-supervised Medical Image Segmentation","date":"2023-11-10","arxiv_id":"2311.06031","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-rock-image-segmentation-in-digital","title":"Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks","date":"2023-11-10","arxiv_id":"2311.06079","repositories_listed":0,"syntology":null},{"url":null,"slug":"eviprompt-a-training-free-evidential-prompt","title":"EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images","date":"2023-11-10","arxiv_id":"2311.06400","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-based-norwegian-tree-species-detection","title":"Lidar-based Norwegian tree species detection using deep learning","date":"2023-11-10","arxiv_id":"2311.06066","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-side-effects-of-oscillations-in","title":"Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks","date":"2023-11-09","arxiv_id":"2311.05109","repositories_listed":0,"syntology":null},{"url":null,"slug":"samvg-a-multi-stage-image-vectorization-model","title":"SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model","date":"2023-11-09","arxiv_id":"2311.05276","repositories_listed":0,"syntology":null},{"url":null,"slug":"seaturtleid2022-a-long-span-dataset-for","title":"SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification","date":"2023-11-09","arxiv_id":"2311.05524","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-foundation-models-efficient-for-medical","title":"Are foundation models efficient for medical image segmentation?","date":"2023-11-08","arxiv_id":"2311.04847","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-what-and-how-of-annotation-in","title":"Learning the What and How of Annotation in Video Object Segmentation","date":"2023-11-08","arxiv_id":"2311.04414","repositories_listed":0,"syntology":null},{"url":null,"slug":"sku-patch-towards-efficient-instance","title":"SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store","date":"2023-11-08","arxiv_id":"2311.04645","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-knowledge-transfer","title":"Supervised domain adaptation for building extraction from off-nadir aerial images","date":"2023-11-07","arxiv_id":"2311.03867","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiclass-segmentation-using-teeth-attention","title":"Multiclass Segmentation using Teeth Attention Modules for Dental X-ray Images","date":"2023-11-07","arxiv_id":"2311.03749","repositories_listed":0,"syntology":null},{"url":"/paper/omnivec-learning-robust-representations-with","slug":"omnivec-learning-robust-representations-with","title":"OmniVec: Learning robust representations with cross modal sharing","date":"2023-11-07","arxiv_id":"2311.05709","repositories_listed":0,"syntology":null},{"url":null,"slug":"cola-coarse-label-multi-source-lidar-semantic","title":"COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving","date":"2023-11-06","arxiv_id":"2311.03017","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-point-annotations-in-segmentation","title":"Leveraging point annotations in segmentation learning with boundary loss","date":"2023-11-06","arxiv_id":"2311.03537","repositories_listed":0,"syntology":null},{"url":null,"slug":"pelvic-floor-mri-segmentation-based-on-semi","title":"Pelvic floor MRI segmentation based on semi-supervised deep learning","date":"2023-11-06","arxiv_id":"2311.03105","repositories_listed":0,"syntology":null},{"url":null,"slug":"seggen-supercharging-segmentation-models-with","title":"SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis","date":"2023-11-06","arxiv_id":"2311.03355","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-drone-collision-hazards-in","title":"Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet","date":"2023-11-06","arxiv_id":"2311.03221","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-region-growing-network-for","title":"Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence","date":"2023-11-06","arxiv_id":"2311.03572","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-uncertainty-in-polygon","title":"An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance","date":"2023-11-05","arxiv_id":"2311.02707","repositories_listed":0,"syntology":null},{"url":null,"slug":"isar-a-benchmark-for-single-and-few-shot","title":"ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification","date":"2023-11-05","arxiv_id":"2311.02734","repositories_listed":0,"syntology":null},{"url":null,"slug":"potholeguard-a-pothole-detection-approach-by","title":"PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation","date":"2023-11-05","arxiv_id":"2311.02641","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssl-dg-rethinking-and-fusing-semi-supervised","title":"SSL-DG: Rethinking and Fusing Semi-supervised Learning and Domain Generalization in Medical Image Segmentation","date":"2023-11-05","arxiv_id":"2311.02583","repositories_listed":0,"syntology":null},{"url":null,"slug":"tfnet-tuning-fork-network-with-neighborhood","title":"TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction","date":"2023-11-05","arxiv_id":"2311.02617","repositories_listed":0,"syntology":null},{"url":null,"slug":"dataset-for-flood-area-recognition-with","title":"Dataset for flood area recognition with semantic segmentation","date":"2023-11-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-experiment-for-semantic","title":"A deep learning experiment for semantic segmentation of overlapping characters in palimpsests","date":"2023-11-02","arxiv_id":"2311.01130","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-is-auto-net-augmentation-driven","title":"Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation","date":"2023-11-02","arxiv_id":"2311.01023","repositories_listed":0,"syntology":null},{"url":null,"slug":"cml-mots-collaborative-multi-task-learning","title":"CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation","date":"2023-11-02","arxiv_id":"2311.00987","repositories_listed":0,"syntology":null},{"url":null,"slug":"memoryseg-online-lidar-semantic-segmentation-1","title":"MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory","date":"2023-11-02","arxiv_id":"2311.01556","repositories_listed":0,"syntology":null},{"url":null,"slug":"overhead-line-defect-recognition-based-on","title":"Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation","date":"2023-11-02","arxiv_id":"2311.00979","repositories_listed":0,"syntology":null},{"url":null,"slug":"scattering-vision-transformer-spectral-mixing","title":"Scattering Vision Transformer: Spectral Mixing Matters","date":"2023-11-02","arxiv_id":"2311.01310","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-based-deep-unsupervised-image","title":"Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts","date":"2023-11-01","arxiv_id":"2311.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"paumer-patch-pausing-transformer-for-semantic","title":"PAUMER: Patch Pausing Transformer for Semantic Segmentation","date":"2023-11-01","arxiv_id":"2311.00586","repositories_listed":0,"syntology":null},{"url":null,"slug":"sdf4chd-generative-modeling-of-cardiac","title":"SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects","date":"2023-11-01","arxiv_id":"2311.00332","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotator-a-generic-active-learning-baseline","title":"Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation","date":"2023-10-31","arxiv_id":"2310.20293","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-depth-prediction-and-semantic","title":"Joint Depth Prediction and Semantic Segmentation with Multi-View SAM","date":"2023-10-31","arxiv_id":"2311.00134","repositories_listed":0,"syntology":null},{"url":null,"slug":"team-i2r-vi-ff-technical-report-on-epic","title":"Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023","date":"2023-10-31","arxiv_id":"2310.20120","repositories_listed":0,"syntology":null},{"url":null,"slug":"view-classification-and-object-detection-in-1","title":"View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning","date":"2023-10-31","arxiv_id":"2311.00068","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-gaussian-splatting-from-markerless","title":"Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements","date":"2023-10-30","arxiv_id":"2310.19441","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-breast-cancer-diagnosis-with","title":"Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images","date":"2023-10-30","arxiv_id":"2310.19411","repositories_listed":0,"syntology":null},{"url":null,"slug":"l2t-dln-learning-to-teach-with-dynamic-loss","title":"L2T-DLN: Learning to Teach with Dynamic Loss Network","date":"2023-10-30","arxiv_id":"2310.19313","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-machine-1","title":"Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI","date":"2023-10-30","arxiv_id":"2312.02167","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-data-augmentations-on-self-semi","title":"Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models","date":"2023-10-28","arxiv_id":"2310.18850","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-localization-and-segmentation-of","title":"One-shot Localization and Segmentation of Medical Images with Foundation Models","date":"2023-10-28","arxiv_id":"2310.18642","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-chebyshev-confidence-guided-source-free","title":"A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation","date":"2023-10-27","arxiv_id":"2310.18087","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-approach-to-land-cover","title":"A Self-Supervised Approach to Land Cover Segmentation","date":"2023-10-27","arxiv_id":"2310.18251","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-augmented-spatial-aware-zero-shot","title":"Text Augmented Spatial-aware Zero-shot Referring Image Segmentation","date":"2023-10-27","arxiv_id":"2310.18049","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-lichen-monitoring-in-ecological","title":"Automating lichen monitoring in ecological studies using instance segmentation of time-lapse images","date":"2023-10-26","arxiv_id":"2310.17080","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-prior-and-posterior-conditional","title":"Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment","date":"2023-10-26","arxiv_id":"2310.17801","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergynet-bridging-the-gap-between-discrete","title":"SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation","date":"2023-10-26","arxiv_id":"2310.17764","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-driven-prompt-evolution-for-foundation","title":"Task-driven Prompt Evolution for Foundation Models","date":"2023-10-26","arxiv_id":"2310.17128","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-note-feasibility-of-translating-3","title":"Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls","date":"2023-10-26","arxiv_id":"2310.17152","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-accessory-try-on-via-keypoint","title":"Virtual Accessory Try-On via Keypoint Hallucination","date":"2023-10-26","arxiv_id":"2310.17131","repositories_listed":0,"syntology":null},{"url":null,"slug":"4d-editor-interactive-object-level-editing-in","title":"4D-Editor: Interactive Object-level Editing in Dynamic Neural Radiance Fields via Semantic Distillation","date":"2023-10-25","arxiv_id":"2310.16858","repositories_listed":0,"syntology":null},{"url":null,"slug":"gramian-attention-heads-are-strong-yet-1","title":"Gramian Attention Heads are Strong yet Efficient Vision Learners","date":"2023-10-25","arxiv_id":"2310.16483","repositories_listed":0,"syntology":null},{"url":null,"slug":"parisluco3d-a-high-quality-target-dataset-for","title":"ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception","date":"2023-10-25","arxiv_id":"2310.16542","repositories_listed":0,"syntology":null},{"url":null,"slug":"rebuild-city-buildings-from-off-nadir-aerial","title":"Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model","date":"2023-10-25","arxiv_id":"2310.16717","repositories_listed":0,"syntology":null},{"url":null,"slug":"s-3-tta-scale-style-selection-for-test-time","title":"S$^3$-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation","date":"2023-10-25","arxiv_id":"2310.16783","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-but-verify-robust-image-segmentation","title":"Trust, but Verify: Robust Image Segmentation using Deep Learning","date":"2023-10-25","arxiv_id":"2310.16999","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-semantic-6","title":"Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement","date":"2023-10-25","arxiv_id":"2310.16979","repositories_listed":0,"syntology":null},{"url":null,"slug":"convbki-real-time-probabilistic-semantic","title":"ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty","date":"2023-10-24","arxiv_id":"2310.16020","repositories_listed":0,"syntology":null},{"url":null,"slug":"cpseg-finer-grained-image-semantic","title":"CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting","date":"2023-10-24","arxiv_id":"2310.16069","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-using-u-net-architecture","title":"Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images","date":"2023-10-24","arxiv_id":"2310.16186","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-level-clustering-network-for","title":"Pixel-Level Clustering Network for Unsupervised Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16234","repositories_listed":0,"syntology":null},{"url":null,"slug":"msformer-a-skeleton-multiview-fusion-method","title":"MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation","date":"2023-10-23","arxiv_id":"2310.14489","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-clip-merging-vision-foundation-models","title":"SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding","date":"2023-10-23","arxiv_id":"2310.15308","repositories_listed":0,"syntology":null},{"url":null,"slug":"spvos-efficient-video-object-segmentation","title":"SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution","date":"2023-10-23","arxiv_id":"2310.15115","repositories_listed":0,"syntology":null},{"url":null,"slug":"competitive-ensembling-teacher-student","title":"Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation","date":"2023-10-21","arxiv_id":"2310.13955","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-individual-tree-crown-detection","title":"A review of individual tree crown detection and delineation from optical remote sensing images","date":"2023-10-20","arxiv_id":"2310.13481","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepfracture-a-generative-approach-for","title":"DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning","date":"2023-10-20","arxiv_id":"2310.13344","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-scale-dependency-modeling-for-skin","title":"Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks","date":"2023-10-20","arxiv_id":"2310.13727","repositories_listed":0,"syntology":null},{"url":null,"slug":"ross-radar-off-road-semantic-segmentation","title":"ROSS: Radar Off-road Semantic Segmentation","date":"2023-10-20","arxiv_id":"2310.13551","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-for-iccv-2023-visual","title":"Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation","date":"2023-10-20","arxiv_id":"2310.13533","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-techniques-for-video-instance","title":"Deep Learning Techniques for Video Instance Segmentation: A Survey","date":"2023-10-19","arxiv_id":"2310.12393","repositories_listed":0,"syntology":null},{"url":null,"slug":"recolorcloud-a-point-cloud-tool-for","title":"RecolorCloud: A Point Cloud Tool for Recoloring, Segmentation, and Conversion","date":"2023-10-19","arxiv_id":"2310.12470","repositories_listed":0,"syntology":null},{"url":null,"slug":"trusted-the-paired-3d-transabdominal","title":"TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research","date":"2023-10-19","arxiv_id":"2310.12646","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-logic-programming-and-kernel-grouping","title":"Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks","date":"2023-10-19","arxiv_id":"2310.13073","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-consistency-guided-source-free","title":"Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation","date":"2023-10-18","arxiv_id":"2310.11766","repositories_listed":0,"syntology":null},{"url":null,"slug":"panoptic-out-of-distribution-segmentation","title":"Panoptic Out-of-Distribution Segmentation","date":"2023-10-18","arxiv_id":"2310.11797","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-segmentation-via-sparse-coding","title":"Medical Image Segmentation via Sparse Coding Decoder","date":"2023-10-17","arxiv_id":"2310.10957","repositories_listed":0,"syntology":null},{"url":null,"slug":"whole-brain-radiomics-for-clustered-federated","title":"Whole-brain radiomics for clustered federated personalization in brain tumor segmentation","date":"2023-10-17","arxiv_id":"2310.11480","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-conditional-shape-models-for-3d-cardiac","title":"Deep Conditional Shape Models for 3D cardiac image segmentation","date":"2023-10-16","arxiv_id":"2310.10756","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-and-improvement-of-segment","title":"Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation","date":"2023-10-16","arxiv_id":"2310.10493","repositories_listed":0,"syntology":null},{"url":null,"slug":"seunet-trans-a-simple-yet-effective-unet","title":"SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation","date":"2023-10-16","arxiv_id":"2310.09998","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-embeddings-scalable-self-supervised","title":"Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision","date":"2023-10-16","arxiv_id":"2401.08581","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-augmentation-with-controlled-diffusion","title":"Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation","date":"2023-10-15","arxiv_id":"2310.09760","repositories_listed":0,"syntology":null},{"url":null,"slug":"tabletop-transparent-scene-reconstruction-via","title":"Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior","date":"2023-10-15","arxiv_id":"2310.09956","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-k-pooling-with-patch-contrastive-learning","title":"Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation","date":"2023-10-15","arxiv_id":"2310.09828","repositories_listed":0,"syntology":null},{"url":null,"slug":"equirectangular-image-construction-method-for","title":"Equirectangular image construction method for standard CNNs for Semantic Segmentation","date":"2023-10-13","arxiv_id":"2310.09122","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-initialization-free-level-set-method-via","title":"Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation","date":"2023-10-13","arxiv_id":"2310.08861","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-multi-modal-3d-semantic","title":"Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving","date":"2023-10-13","arxiv_id":"2310.08826","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-guided-unsupervised-domain-adaptation-for","title":"Learning to Adapt SAM for Segmenting Cross-domain Point Clouds","date":"2023-10-13","arxiv_id":"2310.08820","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultrasound-image-segmentation-of-thyroid","title":"Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration","date":"2023-10-13","arxiv_id":"2310.09221","repositories_listed":0,"syntology":null}],"record_sha256":"b0c2ebc48b1a4ba464af6c62829b7ed4683db66e5111d47762e6623df0023327","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}