{"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/76","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":76,"pages_in_order":131,"rows_per_page":100,"rows":[7501,7600],"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/75","next":"/task/segmentation/papers/77","papers":[{"url":null,"slug":"c-darl-contrastive-diffusion-adversarial","title":"C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation","date":"2023-07-31","arxiv_id":"2308.00193","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-conditional-latent-diffusion-for","title":"Contrastive Conditional Latent Diffusion for Audio-visual Segmentation","date":"2023-07-31","arxiv_id":"2307.16579","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpmix-mixture-of-depth-and-point-cloud-video","title":"DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation","date":"2023-07-31","arxiv_id":"2307.16803","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-learning-with-residual-transformer","title":"Ensemble Learning with Residual Transformer for Brain Tumor Segmentation","date":"2023-07-31","arxiv_id":"2308.00128","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-semi-supervised-learning","title":"Hierarchical Semi-Supervised Learning Framework for Surgical Gesture Segmentation and Recognition Based on Multi-Modality Data","date":"2023-07-31","arxiv_id":"2308.02529","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-driving-heterogeneity-using","title":"Identification of Driving Heterogeneity using Action-chains","date":"2023-07-31","arxiv_id":"2307.16843","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-and-improving-latent-density","title":"Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging","date":"2023-07-31","arxiv_id":"2307.16694","repositories_listed":0,"syntology":null},{"url":null,"slug":"selfseg-a-self-supervised-sub-word","title":"SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation","date":"2023-07-31","arxiv_id":"2307.16400","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unbalanced-motion-part-decoupling","title":"Towards Imbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation","date":"2023-07-31","arxiv_id":"2307.16565","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-centric-deep-learning-approach-to","title":"Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance","date":"2023-07-29","arxiv_id":"2308.00008","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-weakly-supervised-segmentation-network","title":"A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors","date":"2023-07-27","arxiv_id":"2307.14603","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-segmentation-network-for-scene-text","title":"Adaptive Segmentation Network for Scene Text Detection","date":"2023-07-27","arxiv_id":"2307.15029","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepfake-image-generation-for-improved-brain","title":"Deepfake Image Generation for Improved Brain Tumor Segmentation","date":"2023-07-26","arxiv_id":"2307.14273","repositories_listed":0,"syntology":null},{"url":null,"slug":"fluorescent-neuronal-cells-v2-multi-task","title":"Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy","date":"2023-07-26","arxiv_id":"2307.14243","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-with-diffusion-models-for-dental","title":"Pre-Training with Diffusion models for Dental Radiography segmentation","date":"2023-07-26","arxiv_id":"2307.14066","repositories_listed":0,"syntology":null},{"url":"/paper/resolution-aware-design-of-atrous-rates-for","slug":"resolution-aware-design-of-atrous-rates-for","title":"Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks","date":"2023-07-26","arxiv_id":"2307.14179","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-aware-query-enhanced-transformer-for","title":"Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation","date":"2023-07-25","arxiv_id":"2307.13236","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-transferable-object-centric","title":"Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation","date":"2023-07-25","arxiv_id":"2307.13645","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-lobe-segmentation-using-attentive","title":"Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation","date":"2023-07-24","arxiv_id":"2307.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-medical-image-segmentation-4","title":"Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment","date":"2023-07-24","arxiv_id":"2307.12630","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-annotation-strategies-for-segmentation","title":"Sparse annotation strategies for segmentation of short axis cardiac MRI","date":"2023-07-24","arxiv_id":"2307.12619","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-and-semi-supervised-polyp","title":"Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data","date":"2023-07-22","arxiv_id":"2307.12033","repositories_listed":0,"syntology":null},{"url":null,"slug":"cortexmorph-fast-cortical-thickness","title":"CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph","date":"2023-07-21","arxiv_id":"2307.11567","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-modeling-of-inter-and-intra","title":"Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation","date":"2023-07-21","arxiv_id":"2307.11397","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-intervals-for-performance","title":"Confidence Intervals for Performance Estimates in Brain MRI Segmentation","date":"2023-07-20","arxiv_id":"2307.10926","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-semantic-compensation-for","title":"Gradient-Semantic Compensation for Incremental Semantic Segmentation","date":"2023-07-20","arxiv_id":"2307.10822","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-segmentation-for-diverse-gesture","title":"Interactive Segmentation for Diverse Gesture Types Without Context","date":"2023-07-20","arxiv_id":"2307.10518","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-one-sided-synthetic-unpaired-image","title":"Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention","date":"2023-07-20","arxiv_id":"2307.11253","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-calibration-for-semantic-segmentation","title":"Label Calibration for Semantic Segmentation Under Domain Shift","date":"2023-07-20","arxiv_id":"2307.10842","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-feature-distillation-for-network","title":"Quantized Feature Distillation for Network Quantization","date":"2023-07-20","arxiv_id":"2307.10638","repositories_listed":0,"syntology":null},{"url":null,"slug":"clickseg-3d-instance-segmentation-with-click","title":"ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations","date":"2023-07-19","arxiv_id":"2307.09732","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-approaches-to-supervised-image","title":"Two Approaches to Supervised Image Segmentation","date":"2023-07-19","arxiv_id":"2307.10123","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-ce-uncertainty-aware-cross-entropy-for","title":"U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation","date":"2023-07-19","arxiv_id":"2307.09947","repositories_listed":0,"syntology":null},{"url":null,"slug":"cg-fusion-cam-online-segmentation-of-laser","title":"CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics","date":"2023-07-18","arxiv_id":"2307.09161","repositories_listed":0,"syntology":null},{"url":null,"slug":"evil-evidential-inference-learning-for","title":"EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation","date":"2023-07-18","arxiv_id":"2307.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"pottsmgnet-a-mathematical-explanation-of","title":"PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks","date":"2023-07-18","arxiv_id":"2307.09039","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-semantic-segmentation-in","title":"Towards Automated Semantic Segmentation in Mammography Images","date":"2023-07-18","arxiv_id":"2307.10296","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-bevmtn-real-time-lidar-bird-s-eye-view","title":"LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving","date":"2023-07-17","arxiv_id":"2307.08850","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-diffusion-segmentation-model-for","title":"Multimodal Diffusion Segmentation Model for Object Segmentation from Manipulation Instructions","date":"2023-07-17","arxiv_id":"2307.08597","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-real-time-semantic-segmentation-of","title":"On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild","date":"2023-07-17","arxiv_id":"2307.10267","repositories_listed":0,"syntology":null},{"url":"/paper/variational-probabilistic-fusion-network-for","slug":"variational-probabilistic-fusion-network-for","title":"Variational Probabilistic Fusion Network for RGB-T Semantic Segmentation","date":"2023-07-17","arxiv_id":"2307.08536","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-slca-unet-architecture-for-automatic","title":"A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation","date":"2023-07-16","arxiv_id":"2307.08048","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-weighted-logit-consistency-improves","title":"Boundary-weighted logit consistency improves calibration of segmentation networks","date":"2023-07-16","arxiv_id":"2307.08163","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-discovery-by-low-dimensional","title":"Multi-Object Discovery by Low-Dimensional Object Motion","date":"2023-07-16","arxiv_id":"2307.08027","repositories_listed":0,"syntology":null},{"url":null,"slug":"handwritten-and-printed-text-segmentation-a","title":"Handwritten and Printed Text Segmentation: A Signature Case Study","date":"2023-07-15","arxiv_id":"2307.07887","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-pseudo-labeled-segmentation-for","title":"Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting","date":"2023-07-15","arxiv_id":"2307.07677","repositories_listed":0,"syntology":null},{"url":null,"slug":"psgformer-enhancing-3d-point-cloud-instance","title":"PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance","date":"2023-07-15","arxiv_id":"2307.07708","repositories_listed":0,"syntology":null},{"url":null,"slug":"lest-large-scale-lidar-semantic-segmentation","title":"LEST: Large-scale LiDAR Semantic Segmentation with Transformer","date":"2023-07-14","arxiv_id":"2307.09367","repositories_listed":0,"syntology":null},{"url":null,"slug":"mpdiou-a-loss-for-efficient-and-accurate","title":"MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression","date":"2023-07-14","arxiv_id":"2307.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"densemp-unsupervised-dense-pre-training-for","title":"DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation","date":"2023-07-13","arxiv_id":"2307.09604","repositories_listed":0,"syntology":null},{"url":null,"slug":"rvd-a-handheld-device-based-fundus-video","title":"RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation","date":"2023-07-13","arxiv_id":"2307.06577","repositories_listed":0,"syntology":null},{"url":null,"slug":"grain-and-grain-boundary-segmentation-using","title":"Grain and Grain Boundary Segmentation using Machine Learning with Real and Generated Datasets","date":"2023-07-12","arxiv_id":"2307.05911","repositories_listed":0,"syntology":null},{"url":null,"slug":"og-equip-vision-occupancy-with-instance","title":"OG: Equip vision occupancy with instance segmentation and visual grounding","date":"2023-07-12","arxiv_id":"2307.05873","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-detection-of-roof-sections-from-a-single","title":"3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction","date":"2023-07-11","arxiv_id":"2307.05409","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-transformer-encoder-to-improve","title":"A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma","date":"2023-07-11","arxiv_id":"2307.05800","repositories_listed":0,"syntology":null},{"url":null,"slug":"argumentative-segmentation-enhancement-for","title":"Argumentative Segmentation Enhancement for Legal Summarization","date":"2023-07-11","arxiv_id":"2307.05081","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathrm-sam-med-a-medical-image-annotation","title":"$\\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model","date":"2023-07-11","arxiv_id":"2307.05617","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-hierarchical-transformers-for-pedestrian","title":"Non-Hierarchical Transformers for Pedestrian Segmentation","date":"2023-07-11","arxiv_id":"2311.02506","repositories_listed":0,"syntology":null},{"url":"/paper/fodvid-flow-guided-object-discovery-in-videos","slug":"fodvid-flow-guided-object-discovery-in-videos","title":"FODVid: Flow-guided Object Discovery in Videos","date":"2023-07-10","arxiv_id":"2307.04392","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-yolop-quantization-aware-you-only-look-once","title":"Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception","date":"2023-07-10","arxiv_id":"2307.04537","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-building-semantic-segmentation","title":"Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets","date":"2023-07-09","arxiv_id":"2307.04101","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-and-road-segmentation-using-effunet","title":"Building and Road Segmentation Using EffUNet and Transfer Learning Approach","date":"2023-07-08","arxiv_id":"2307.03980","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-active-contour-model-for-delineating","title":"A Deep Active Contour Model for Delineating Glacier Calving Fronts","date":"2023-07-07","arxiv_id":"2307.03461","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-self-supervised-vision-1","title":"Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation","date":"2023-07-07","arxiv_id":"2307.03407","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-of-intensity-standardization-on-deep","title":"Effect of Intensity Standardization on Deep Learning for WML Segmentation in Multi-Centre FLAIR MRI","date":"2023-07-07","arxiv_id":"2307.03827","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-look-at-the-current-usage-of","title":"A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task","date":"2023-07-06","arxiv_id":"2307.02862","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-analysis-of-a-segmentation","title":"Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging","date":"2023-07-06","arxiv_id":"2307.03266","repositories_listed":0,"syntology":null},{"url":null,"slug":"segnetr-rethinking-the-local-global","title":"SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks","date":"2023-07-06","arxiv_id":"2307.02953","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-aware-loss-for-aorta-and-great","title":"Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images","date":"2023-07-06","arxiv_id":"2307.03137","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-segmentation-of-brain-white-matter","title":"Direct segmentation of brain white matter tracts in diffusion MRI","date":"2023-07-05","arxiv_id":"2307.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"gnep-based-dynamic-segmentation-and-motion","title":"GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging","date":"2023-07-05","arxiv_id":"2307.02595","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-prototypes-for-open-set-semantic","title":"Multi-Modal Prototypes for Open-World Semantic Segmentation","date":"2023-07-05","arxiv_id":"2307.02003","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-diffusion-representations-for-cross","title":"Prompting Diffusion Representations for Cross-Domain Semantic Segmentation","date":"2023-07-05","arxiv_id":"2307.02138","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-identification-a-self-supervision-task","title":"Source Identification: A Self-Supervision Task for Dense Prediction","date":"2023-07-05","arxiv_id":"2307.02238","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-feature-pyramid-networks-for","title":"Spherical Feature Pyramid Networks For Semantic Segmentation","date":"2023-07-05","arxiv_id":"2307.02658","repositories_listed":0,"syntology":null},{"url":null,"slug":"toothsegnet-image-degradation-meets-tooth","title":"ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images","date":"2023-07-05","arxiv_id":"2307.01979","repositories_listed":0,"syntology":null},{"url":null,"slug":"zju-reler-submission-for-epic-kitchen","title":"ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation","date":"2023-07-05","arxiv_id":"2307.02010","repositories_listed":0,"syntology":null},{"url":null,"slug":"zju-reler-submission-for-epic-kitchen-1","title":"ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single Object Tracking","date":"2023-07-05","arxiv_id":"2307.02508","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-aware-multi-task-network-for-integrating","title":"Edge-aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI","date":"2023-07-04","arxiv_id":"2307.01798","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-richness-of-learned-compressed","title":"Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation","date":"2023-07-04","arxiv_id":"2307.01524","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-on-3d-point-clouds-with","title":"Semantic Segmentation on 3D Point Clouds with High Density Variations","date":"2023-07-04","arxiv_id":"2307.01489","repositories_listed":0,"syntology":null},{"url":null,"slug":"synchronous-image-label-diffusion-probability","title":"Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT","date":"2023-07-04","arxiv_id":"2307.01740","repositories_listed":0,"syntology":null},{"url":null,"slug":"cgam-click-guided-attention-module-for","title":"CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement","date":"2023-07-03","arxiv_id":"2307.01015","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-reliable-pixel-level-labels-for","title":"Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation","date":"2023-07-03","arxiv_id":"2307.00893","repositories_listed":0,"syntology":null},{"url":null,"slug":"met-a-graph-transformer-for-semantic","title":"MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes","date":"2023-07-03","arxiv_id":"2307.01115","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-learning-framework-for-carotid","title":"A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification","date":"2023-07-02","arxiv_id":"2307.00583","repositories_listed":0,"syntology":null},{"url":null,"slug":"pay-attention-to-the-atlas-atlas-guided-test","title":"Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation","date":"2023-07-02","arxiv_id":"2307.00676","repositories_listed":0,"syntology":null},{"url":null,"slug":"referring-video-object-segmentation-with","title":"Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation","date":"2023-07-02","arxiv_id":"2307.00536","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-in-sam-from-weak-annotation-to-pixel-wise","title":"All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning","date":"2023-07-01","arxiv_id":"2307.00290","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-angiogram-trivializing-retinal-vessel","title":"Deep Angiogram: Trivializing Retinal Vessel Segmentation","date":"2023-07-01","arxiv_id":"2307.00245","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-rgb-d-level-segmentation","title":"Achieving RGB-D level Segmentation Performance from a Single ToF Camera","date":"2023-06-30","arxiv_id":"2306.17636","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-progressive-text-prompt-network","title":"Multiscale Progressive Text Prompt Network for Medical Image Segmentation","date":"2023-06-30","arxiv_id":"2307.00174","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-lidar-configurations-on-off-road","title":"Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance","date":"2023-06-28","arxiv_id":"2306.16551","repositories_listed":0,"syntology":null},{"url":null,"slug":"chan-vese-attention-u-net-an-attention","title":"Chan-Vese Attention U-Net: An attention mechanism for robust segmentation","date":"2023-06-28","arxiv_id":"2306.16098","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-transfer-of-pretrained-large-visual","title":"Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection","date":"2023-06-28","arxiv_id":"2306.16186","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-marching-energy-cnn","title":"Fast Marching Energy CNN","date":"2023-06-28","arxiv_id":"2306.16109","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-on-food-instance","title":"Incremental Learning on Food Instance Segmentation","date":"2023-06-28","arxiv_id":"2306.15910","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-navigation-benchmarking-and","title":"Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation","date":"2023-06-27","arxiv_id":"2306.15517","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-dense-correlation-distillation-1","title":"Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract","date":"2023-06-27","arxiv_id":"2306.15278","repositories_listed":0,"syntology":null},{"url":null,"slug":"nano1d-an-accurate-computer-vision-model-for","title":"Nano1D: An accurate Computer Vision software for analysis and segmentation of low-dimensional nanostructures","date":"2023-06-27","arxiv_id":"2306.15319","repositories_listed":0,"syntology":null}],"record_sha256":"59fadaa95066b69a7a0e1e759d4963d6d21f17b3e1d719f8b410f2ac0ac498d5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}