{"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/73","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":73,"pages_in_order":131,"rows_per_page":100,"rows":[7201,7300],"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/72","next":"/task/segmentation/papers/74","papers":[{"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":"/paper/silc-improving-vision-language-pretraining","slug":"silc-improving-vision-language-pretraining","title":"SILC: Improving Vision Language Pretraining with Self-Distillation","date":"2023-10-20","arxiv_id":"2310.13355","repositories_listed":0,"syntology":null},{"url":null,"slug":"2d-3d-interlaced-transformer-for-point-cloud-1","title":"2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision","date":"2023-10-19","arxiv_id":"2310.12817","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":"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":"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":"understanding-video-transformers-for","title":"Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability","date":"2023-10-18","arxiv_id":"2310.12296","repositories_listed":0,"syntology":null},{"url":null,"slug":"vq-nerf-neural-reflectance-decomposition-and","title":"VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization","date":"2023-10-18","arxiv_id":"2310.11864","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-learning-semantic-aware-unsupervised","title":"Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration","date":"2023-10-17","arxiv_id":"2310.11040","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-building-and-road-detection","title":"High-Resolution Building and Road Detection from Sentinel-2","date":"2023-10-17","arxiv_id":"2310.11622","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-form-simultaneous-speech-translation","title":"Long-form Simultaneous Speech Translation: Thesis Proposal","date":"2023-10-17","arxiv_id":"2310.11141","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-scale-spatial-transformer-u-net-for","title":"A Multi-Scale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3D Nuclear Cardiac Images","date":"2023-10-16","arxiv_id":"2310.10095","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-encoder-decoder-architectures-for","title":"Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation","date":"2023-10-16","arxiv_id":"2310.10002","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":"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":"segment-anything-model-for-pedestrian","title":"Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types","date":"2023-10-15","arxiv_id":"2310.09918","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":"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":"timestamp-supervised-wearable-based-activity","title":"Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport","date":"2023-10-13","arxiv_id":"2310.09114","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},{"url":null,"slug":"fine-grained-annotation-for-face-anti","title":"Fine-Grained Annotation for Face Anti-Spoofing","date":"2023-10-12","arxiv_id":"2310.08142","repositories_listed":0,"syntology":null},{"url":null,"slug":"volumetric-medical-image-segmentation-via","title":"Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors","date":"2023-10-12","arxiv_id":"2310.08084","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-for-lightweight-semantic-segmentation","title":"CLIP for Lightweight Semantic Segmentation","date":"2023-10-11","arxiv_id":"2310.07394","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-label-types-on-training-swin-models","title":"Impact of Label Types on Training SWIN Models with Overhead Imagery","date":"2023-10-11","arxiv_id":"2310.07572","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-explainable-skin-lesion","title":"Multi-task Explainable Skin Lesion Classification","date":"2023-10-11","arxiv_id":"2310.07209","repositories_listed":0,"syntology":null},{"url":null,"slug":"s4c-self-supervised-semantic-scene-completion","title":"S4C: Self-Supervised Semantic Scene Completion with Neural Fields","date":"2023-10-11","arxiv_id":"2310.07522","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-evaluation-of-the-segment-anything","title":"Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation","date":"2023-10-09","arxiv_id":"2310.06162","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-prostate-cancer-diagnosis-with-deep","title":"Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification","date":"2023-10-09","arxiv_id":"2310.05371","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-side-tuning-for-vision","title":"Hierarchical Side-Tuning for Vision Transformers","date":"2023-10-09","arxiv_id":"2310.05393","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-accuracy-and-cost-saving-active-learning","title":"High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation","date":"2023-10-09","arxiv_id":"2310.05638","repositories_listed":0,"syntology":null},{"url":"/paper/m3fpolypsegnet-segmentation-network-with","slug":"m3fpolypsegnet-segmentation-network-with","title":"M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images","date":"2023-10-09","arxiv_id":"2310.05538","repositories_listed":0,"syntology":null},{"url":null,"slug":"retseg-retention-based-colorectal-polyps","title":"RetSeg: Retention-based Colorectal Polyps Segmentation Network","date":"2023-10-09","arxiv_id":"2310.05446","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-through-pseudolabels-in","title":"Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation","date":"2023-10-08","arxiv_id":"2310.05990","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-aware-field-to-field-transformations","title":"Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation","date":"2023-10-08","arxiv_id":"2310.05133","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-preserving-instance-segmentation","title":"Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform","date":"2023-10-08","arxiv_id":"2310.05262","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-constrained-semantic-segmentation-for","title":"Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery","date":"2023-10-07","arxiv_id":"2310.04721","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-dynamic-and-small-objects-refinement","title":"Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation","date":"2023-10-07","arxiv_id":"2310.04747","repositories_listed":0,"syntology":null},{"url":null,"slug":"transcc-transformer-network-for-coronary","title":"TransCC: Transformer Network for Coronary Artery CCTA Segmentation","date":"2023-10-07","arxiv_id":"2310.04779","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deeply-supervised-semantic-segmentation","title":"A Deeply Supervised Semantic Segmentation Method Based on GAN","date":"2023-10-06","arxiv_id":"2310.04081","repositories_listed":0,"syntology":null},{"url":null,"slug":"cupre-cross-domain-unsupervised-pre-training","title":"CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation","date":"2023-10-06","arxiv_id":"2310.03981","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffprompter-differentiable-implicit-visual","title":"DiffPrompter: Differentiable Implicit Visual Prompts for Semantic-Segmentation in Adverse Conditions","date":"2023-10-06","arxiv_id":"2310.04181","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-longitudinal-thermal","title":"Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas","date":"2023-10-06","arxiv_id":"2310.04247","repositories_listed":0,"syntology":null},{"url":null,"slug":"ablation-study-to-clarify-the-mechanism-of","title":"Ablation Study to Clarify the Mechanism of Object Segmentation in Multi-Object Representation Learning","date":"2023-10-05","arxiv_id":"2310.03273","repositories_listed":0,"syntology":null},{"url":null,"slug":"ammonia-net-a-multi-task-joint-learning-model","title":"Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis","date":"2023-10-05","arxiv_id":"2310.03472","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-datasets-with-different-label-sets","title":"Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification","date":"2023-10-05","arxiv_id":"2310.03346","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-dino-emergent-properties-and","title":"Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery","date":"2023-10-05","arxiv_id":"2310.03513","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-metacognitive-approach-to-out-of","title":"A Metacognitive Approach to Out-of-Distribution Detection for Segmentation","date":"2023-10-04","arxiv_id":"2311.07578","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-multimodal-segmentation-in","title":"Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models","date":"2023-10-04","arxiv_id":"2310.12995","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-is-also-a-good-teacher-a-new-learning","title":"CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation","date":"2023-10-03","arxiv_id":"2310.02296","repositories_listed":0,"syntology":null},{"url":null,"slug":"coralvos-dataset-and-benchmark-for-coral","title":"CoralVOS: Dataset and Benchmark for Coral Video Segmentation","date":"2023-10-03","arxiv_id":"2310.01946","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-prompt-fine-tuning-of-foundation-models","title":"Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation","date":"2023-10-03","arxiv_id":"2310.02381","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocu-net-a-novel-u-net-architecture-for","title":"OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation","date":"2023-10-03","arxiv_id":"2310.02486","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-adversarial-local-distribution","title":"Cross-adversarial local distribution regularization for semi-supervised medical image segmentation","date":"2023-10-02","arxiv_id":"2310.01176","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-remote-sensing-segmentation-with","title":"Efficient Remote Sensing Segmentation With Generative Adversarial Transformer","date":"2023-10-02","arxiv_id":"2310.01292","repositories_listed":0,"syntology":null},{"url":null,"slug":"elastic-interaction-energy-loss-for-traffic","title":"Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception","date":"2023-10-02","arxiv_id":"2310.01449","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-image-segmentation-based-on-grayscale","title":"Quantum Image Segmentation Based on Grayscale Morphology","date":"2023-10-02","arxiv_id":"2311.11952","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-any-building","title":"Segment Any Building","date":"2023-10-02","arxiv_id":"2310.01164","repositories_listed":0,"syntology":null},{"url":null,"slug":"stars-zero-shot-sim-to-real-transfer-for","title":"STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery","date":"2023-10-02","arxiv_id":"2310.01667","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantum-moving-target-segmentation","title":"A quantum moving target segmentation algorithm for grayscale video","date":"2023-10-01","arxiv_id":"2310.03038","repositories_listed":0,"syntology":null},{"url":null,"slug":"completing-visual-objects-via-bridging","title":"Completing Visual Objects via Bridging Generation and Segmentation","date":"2023-10-01","arxiv_id":"2310.00808","repositories_listed":0,"syntology":null},{"url":null,"slug":"propagating-semantic-labels-in-video-data","title":"Propagating Semantic Labels in Video Data","date":"2023-10-01","arxiv_id":"2310.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-augmented-transformer-network-for-weakly","title":"Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentation","date":"2023-09-30","arxiv_id":"2310.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-sam-ablations-for-enhancing-medical","title":"Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology","date":"2023-09-30","arxiv_id":"2310.00504","repositories_listed":0,"syntology":null},{"url":null,"slug":"pubic-symphysis-fetal-head-segmentation-using","title":"Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention","date":"2023-09-30","arxiv_id":"2310.00289","repositories_listed":0,"syntology":null},{"url":null,"slug":"unilvseg-unified-left-ventricular","title":"SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning","date":"2023-09-30","arxiv_id":"2310.00454","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-in-kidney-biopsy-structural","title":"Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation","date":"2023-09-29","arxiv_id":"2309.17166","repositories_listed":0,"syntology":null},{"url":null,"slug":"benefits-of-mirror-weight-symmetry-for-3d","title":"Benefits of mirror weight symmetry for 3D mesh segmentation in biomedical applications","date":"2023-09-29","arxiv_id":"2309.17076","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-and-deep-learning","title":"Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data","date":"2023-09-29","arxiv_id":"2309.16968","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-activation-map-based-weakly-supervised","title":"Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images","date":"2023-09-28","arxiv_id":"2309.16627","repositories_listed":0,"syntology":null},{"url":null,"slug":"photonic-accelerators-for-image-segmentation","title":"Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection","date":"2023-09-28","arxiv_id":"2309.16783","repositories_listed":0,"syntology":null},{"url":null,"slug":"radar-instance-transformer-reliable-moving","title":"Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds","date":"2023-09-28","arxiv_id":"2309.16435","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixel-transformers-for-efficient","title":"Superpixel Transformers for Efficient Semantic Segmentation","date":"2023-09-28","arxiv_id":"2309.16889","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-step-active-learning-for-instance","title":"Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling","date":"2023-09-28","arxiv_id":"2309.16139","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-eosinophil","title":"Uncertainty Quantification for Eosinophil Segmentation","date":"2023-09-28","arxiv_id":"2309.16536","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-in-context-learning-for-few-shot","title":"Visual In-Context Learning for Few-Shot Eczema Segmentation","date":"2023-09-28","arxiv_id":"2309.16656","repositories_listed":0,"syntology":null},{"url":null,"slug":"voting-network-for-contour-levee-farmland","title":"Voting Network for Contour Levee Farmland Segmentation and Classification","date":"2023-09-28","arxiv_id":"2309.16561","repositories_listed":0,"syntology":null},{"url":null,"slug":"factorized-diffusion-architectures-for","title":"Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation","date":"2023-09-27","arxiv_id":"2309.15726","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-sam-harnessing-a-segmentation","title":"Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularization","date":"2023-09-27","arxiv_id":"2309.15562","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-topology-for-domain-adaptive-road","title":"Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery","date":"2023-09-27","arxiv_id":"2309.15625","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-beyond-the-patch-scale-adaptive-1","title":"Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning","date":"2023-09-27","arxiv_id":"2309.15372","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-transfer-and-self-supervised-learning","title":"Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation","date":"2023-09-27","arxiv_id":"2309.15485","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-robust-semantic-segmentation-uncv2023","title":"The Robust Semantic Segmentation UNCV2023 Challenge Results","date":"2023-09-27","arxiv_id":"2309.15478","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-city-matters-a-multimodal-remote","title":"Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks","date":"2023-09-26","arxiv_id":"2309.16499","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-continual-learning-object","title":"Memory-Efficient Continual Learning Object Segmentation for Long Video","date":"2023-09-26","arxiv_id":"2309.15274","repositories_listed":0,"syntology":null},{"url":null,"slug":"thalamic-nuclei-segmentation-from-t-1","title":"Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts","date":"2023-09-26","arxiv_id":"2309.15053","repositories_listed":0,"syntology":null},{"url":null,"slug":"zico-bc-a-bias-corrected-zero-shot-nas-for","title":"ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks","date":"2023-09-26","arxiv_id":"2309.14666","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-on-video-object","title":"Adversarial Attacks on Video Object Segmentation with Hard Region Discovery","date":"2023-09-25","arxiv_id":"2309.13857","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-and-pooling-based-sigmoid-colon","title":"Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images","date":"2023-09-25","arxiv_id":"2309.13872","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-animation-of-hair-blowing-in-still","title":"Automatic Animation of Hair Blowing in Still Portrait Photos","date":"2023-09-25","arxiv_id":"2309.14207","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-generalization-of-white-matter-tract","title":"Better Generalization of White Matter Tract Segmentation to Arbitrary Datasets with Scaled Residual Bootstrap","date":"2023-09-25","arxiv_id":"2309.13980","repositories_listed":0,"syntology":null},{"url":null,"slug":"memo-dataset-and-methods-for-robust","title":"MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences","date":"2023-09-25","arxiv_id":"2309.14550","repositories_listed":0,"syntology":null},{"url":null,"slug":"mma-net-multiple-morphology-aware-network-for","title":"MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement","date":"2023-09-25","arxiv_id":"2309.13817","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-test-time-adaptation-for","title":"Single Image Test-Time Adaptation for Segmentation","date":"2023-09-25","arxiv_id":"2309.14052","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sam-based-solution-for-hierarchical","title":"A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition","date":"2023-09-24","arxiv_id":"2309.13578","repositories_listed":0,"syntology":null},{"url":null,"slug":"logicseg-parsing-visual-semantics-with-neural","title":"LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning","date":"2023-09-24","arxiv_id":"2309.13556","repositories_listed":0,"syntology":null},{"url":null,"slug":"oneseg-self-learning-and-one-shot-learning","title":"OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation","date":"2023-09-24","arxiv_id":"2309.13671","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mirror-unet-architecture-for-pet-ct-lesion","title":"A mirror-Unet architecture for PET/CT lesion segmentation","date":"2023-09-23","arxiv_id":"2309.13398","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-aware-learning-for-3d-point-cloud","title":"Edge Aware Learning for 3D Point Cloud","date":"2023-09-23","arxiv_id":"2309.13472","repositories_listed":0,"syntology":null}],"record_sha256":"75064c8bea08f4478b03fc18c81a521ae3a104f7075007f9c6cfc1ec5647dfb2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}