{"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/97","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":97,"pages_in_order":148,"rows_per_page":100,"rows":[9601,9700],"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/96","next":"/task/semantic-segmentation/papers/98","papers":[{"url":null,"slug":"semantic-segmentation-using-vision","title":"Semantic Segmentation using Vision Transformers: A survey","date":"2023-05-05","arxiv_id":"2305.03273","repositories_listed":0,"syntology":null},{"url":null,"slug":"haista-net-human-assisted-instance","title":"HAISTA-NET: Human Assisted Instance Segmentation Through Attention","date":"2023-05-04","arxiv_id":"2305.03105","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-undisciplined-over-smoothing-in","title":"Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation","date":"2023-05-04","arxiv_id":"2305.03112","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtlsegformer-multi-task-learning-with","title":"MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture","date":"2023-05-04","arxiv_id":"2305.02813","repositories_listed":0,"syntology":null},{"url":null,"slug":"point2tree-p2t-framework-for-parameter-tuning","title":"Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest","date":"2023-05-04","arxiv_id":"2305.02651","repositories_listed":0,"syntology":null},{"url":null,"slug":"updexplainer-an-interpretable-transformer","title":"UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery","date":"2023-05-04","arxiv_id":"2305.02911","repositories_listed":0,"syntology":null},{"url":null,"slug":"urbanbis-a-large-scale-benchmark-for-fine","title":"UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation","date":"2023-05-04","arxiv_id":"2305.02627","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributional-instance-segmentation-modeling","title":"Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN","date":"2023-05-03","arxiv_id":"2305.01910","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-vision-transformer-layer-choosing","title":"Exploring vision transformer layer choosing for semantic segmentation","date":"2023-05-02","arxiv_id":"2305.01279","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-lidar-fields-for-novel-view-synthesis","title":"Neural LiDAR Fields for Novel View Synthesis","date":"2023-05-02","arxiv_id":"2305.01643","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-is-a-good-pseudo-label","title":"Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation","date":"2023-05-02","arxiv_id":"2305.01275","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-s-4-language-guided-self-supervised","title":"CLIP-S$^4$: Language-Guided Self-Supervised Semantic Segmentation","date":"2023-05-01","arxiv_id":"2305.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-semantic-segmentation","title":"Point Cloud Semantic Segmentation","date":"2023-05-01","arxiv_id":"2305.00773","repositories_listed":0,"syntology":null},{"url":null,"slug":"prseg-a-lightweight-patch-rotate-mlp-decoder","title":"PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation","date":"2023-05-01","arxiv_id":"2305.00671","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-novelties-with-empty-classes","title":"Detecting Novelties with Empty Classes","date":"2023-04-30","arxiv_id":"2305.00983","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structured-output-representations","title":"Learning Structured Output Representations from Attributes using Deep Conditional Generative Models","date":"2023-04-30","arxiv_id":"2305.00980","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-segmentation-from-mri-images","title":"Brain Tumor Segmentation from MRI Images using Deep Learning Techniques","date":"2023-04-29","arxiv_id":"2305.00257","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularizing-self-training-for-unsupervised","title":"Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints","date":"2023-04-29","arxiv_id":"2305.00131","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-model-sam-meets-glass-mirror","title":"Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected","date":"2023-04-29","arxiv_id":"2305.00278","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensor-equivariance-by-lidar-projection","title":"Sensor Equivariance by LiDAR Projection Images","date":"2023-04-29","arxiv_id":"2305.00221","repositories_listed":0,"syntology":null},{"url":null,"slug":"diamant-dual-image-attention-map-encoders-for","title":"DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation","date":"2023-04-28","arxiv_id":"2304.14571","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-sensor-layouts-for-end-to-end","title":"Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters","date":"2023-04-28","arxiv_id":"2304.14736","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-processing-training-data-improves","title":"Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation","date":"2023-04-28","arxiv_id":"2304.14625","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-adaptive-split-federated-learning-for","title":"Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations","date":"2023-04-28","arxiv_id":"2304.14976","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-meets-robotic-surgery-an-empirical-study","title":"SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective","date":"2023-04-28","arxiv_id":"2304.14674","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-on-medical-images-a-comprehensive-study","title":"SAM on Medical Images: A Comprehensive Study on Three Prompt Modes","date":"2023-04-28","arxiv_id":"2305.00035","repositories_listed":0,"syntology":null},{"url":null,"slug":"scope-structural-continuity-preservation-for","title":"SCOPE: Structural Continuity Preservation for Medical Image Segmentation","date":"2023-04-28","arxiv_id":"2304.14572","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-feature-fusion-attention-network-for","title":"Dual-feature Fusion Attention Network for Small Object Segmentation","date":"2023-04-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-semantic-segmentation-using-millimeter","title":"Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds","date":"2023-04-27","arxiv_id":"2304.14132","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-implicit-dense-semantic-slam","title":"Neural Implicit Dense Semantic SLAM","date":"2023-04-27","arxiv_id":"2304.14560","repositories_listed":0,"syntology":null},{"url":null,"slug":"skinsam-empowering-skin-cancer-segmentation","title":"SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model","date":"2023-04-27","arxiv_id":"2304.13973","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-entropy-active-domain-adaptation-in","title":"Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13513","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-cnns-for-semantic-segmentation","title":"Exploiting CNNs for Semantic Segmentation with Pascal VOC","date":"2023-04-26","arxiv_id":"2304.13216","repositories_listed":0,"syntology":null},{"url":null,"slug":"fvp-fourier-visual-prompting-for-source-free","title":"FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13672","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixing-data-augmentation-with-preserving","title":"Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13490","repositories_listed":0,"syntology":null},{"url":null,"slug":"methods-and-datasets-for-segmentation-of","title":"Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art","date":"2023-04-25","arxiv_id":"2304.13014","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-from-space","title":"Segment anything, from space?","date":"2023-04-25","arxiv_id":"2304.13000","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-temporal-analysis-of","title":"Self-Supervised Temporal Analysis of Spatiotemporal Data","date":"2023-04-25","arxiv_id":"2304.13143","repositories_listed":0,"syntology":null},{"url":null,"slug":"stm-unet-an-efficient-u-shaped-architecture","title":"STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation","date":"2023-04-25","arxiv_id":"2304.12615","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-potential-of-visual-chatgpt-for-remote","title":"The Potential of Visual ChatGPT For Remote Sensing","date":"2023-04-25","arxiv_id":"2304.13009","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-efficient-event-based-semantic","title":"Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network","date":"2023-04-24","arxiv_id":"2304.11857","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-based-domain-generalization-for","title":"Augmentation-based Domain Generalization for Semantic Segmentation","date":"2023-04-24","arxiv_id":"2304.12122","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-efficient-memory-network-for-video","title":"Robust and Efficient Memory Network for Video Object Segmentation","date":"2023-04-24","arxiv_id":"2304.11840","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-on-unsupervised-domain-adaptation-for","title":"Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving","date":"2023-04-24","arxiv_id":"2304.11928","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-aware-focal-loss-for-3d-image","title":"Topology-Aware Focal Loss for 3D Image Segmentation","date":"2023-04-24","arxiv_id":"2304.12223","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-in-non-euclidean-domains","title":"Segment Anything in Non-Euclidean Domains: Challenges and Opportunities","date":"2023-04-23","arxiv_id":"2304.11595","repositories_listed":0,"syntology":null},{"url":null,"slug":"incomplete-multimodal-learning-for-remote","title":"Incomplete Multimodal Learning for Remote Sensing Data Fusion","date":"2023-04-22","arxiv_id":"2304.11381","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-attention-unet-a-network-model-with","title":"Deep Attention Unet: A Network Model with Global Feature Perception Ability","date":"2023-04-21","arxiv_id":"2304.10829","repositories_listed":0,"syntology":null},{"url":null,"slug":"sss3d-fast-neural-architecture-search-for","title":"SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation","date":"2023-04-21","arxiv_id":"2304.11207","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensembling-instance-and-semantic-segmentation","title":"Ensembling Instance and Semantic Segmentation for Panoptic Segmentation","date":"2023-04-20","arxiv_id":"2304.10326","repositories_listed":0,"syntology":null},{"url":null,"slug":"baybayin-character-instance-detection","title":"Baybayin Character Instance Detection","date":"2023-04-19","arxiv_id":"2304.09469","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-reference-transformer-for-few-shot","title":"Few-shot Medical Image Segmentation via Cross-Reference Transformer","date":"2023-04-19","arxiv_id":"2304.09630","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcelanm-net-medical-image-segmentation-based","title":"DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner","date":"2023-04-19","arxiv_id":"2304.09620","repositories_listed":0,"syntology":null},{"url":null,"slug":"realistic-data-enrichment-for-robust-image","title":"Realistic Data Enrichment for Robust Image Segmentation in Histopathology","date":"2023-04-19","arxiv_id":"2304.09534","repositories_listed":0,"syntology":null},{"url":null,"slug":"accuracy-of-segment-anything-model-sam-in","title":"Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets","date":"2023-04-18","arxiv_id":"2304.09324","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-stylization-modulation-for-domain","title":"Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation","date":"2023-04-18","arxiv_id":"2304.09347","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-state-alignment-for-video-semantic","title":"Motion-state Alignment for Video Semantic Segmentation","date":"2023-04-18","arxiv_id":"2304.08820","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceive-excavate-and-purify-a-novel-object","title":"Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation","date":"2023-04-18","arxiv_id":"2304.08826","repositories_listed":0,"syntology":null},{"url":null,"slug":"udtiri-an-open-source-road-pothole-detection","title":"UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite","date":"2023-04-18","arxiv_id":"2304.08842","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-few-shot-class-incremental","title":"A Survey on Few-Shot Class-Incremental Learning","date":"2023-04-17","arxiv_id":"2304.08130","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-tumour-graph-learning-for-survival","title":"Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients","date":"2023-04-17","arxiv_id":"2304.08106","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-mr-image-segmentation-method-for","title":"Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism","date":"2023-04-17","arxiv_id":"2304.08072","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-sam-meets-medical-images-an","title":"When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation","date":"2023-04-17","arxiv_id":"2304.08506","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-enhanced-feature-learning-for-scene","title":"Region-Enhanced Feature Learning for Scene Semantic Segmentation","date":"2023-04-15","arxiv_id":"2304.07486","repositories_listed":0,"syntology":null},{"url":null,"slug":"comal-conditional-maximum-likelihood-approach","title":"CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation","date":"2023-04-14","arxiv_id":"2304.07372","repositories_listed":0,"syntology":null},{"url":"/paper/mvp-seg-multi-view-prompt-learning-for-open","slug":"mvp-seg-multi-view-prompt-learning-for-open","title":"MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation","date":"2023-04-14","arxiv_id":"2304.06957","repositories_listed":0,"syntology":null},{"url":null,"slug":"cls-token-is-all-you-need-for-zero-shot","title":"[CLS] Token is All You Need for Zero-Shot Semantic Segmentation","date":"2023-04-13","arxiv_id":"2304.06212","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamite-dynamic-query-bootstrapping-for","title":"DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer","date":"2023-04-13","arxiv_id":"2304.06668","repositories_listed":0,"syntology":null},{"url":null,"slug":"stu-net-scalable-and-transferable-medical","title":"STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training","date":"2023-04-13","arxiv_id":"2304.06716","repositories_listed":0,"syntology":null},{"url":null,"slug":"duformer-a-novel-architecture-for-power-line","title":"DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation","date":"2023-04-12","arxiv_id":"2304.05821","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-semantic-segmentation-a-review-of","title":"Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges","date":"2023-04-12","arxiv_id":"2304.05832","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-pseudo-depth-on-open-world-object","title":"Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance","date":"2023-04-12","arxiv_id":"2304.05716","repositories_listed":0,"syntology":null},{"url":null,"slug":"med-vt-multiscale-encoder-decoder-video","title":"MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer","date":"2023-04-12","arxiv_id":"2304.05930","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-field-conditioning-strategies-for-2d","title":"Neural Field Conditioning Strategies for 2D Semantic Segmentation","date":"2023-04-12","arxiv_id":"2304.14371","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixelgraph-semi-automatic-generation-of","title":"SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks","date":"2023-04-12","arxiv_id":"2304.05661","repositories_listed":0,"syntology":null},{"url":"/paper/a-billion-scale-foundation-model-for-remote","slug":"a-billion-scale-foundation-model-for-remote","title":"A Billion-scale Foundation Model for Remote Sensing Images","date":"2023-04-11","arxiv_id":"2304.05215","repositories_listed":0,"syntology":null},{"url":null,"slug":"amortized-learning-of-dynamic-feature-scaling","title":"Scale-Space Hypernetworks for Efficient Biomedical Imaging","date":"2023-04-11","arxiv_id":"2304.05448","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-semantic-segmentation-with","title":"Continual Semantic Segmentation with Automatic Memory Sample Selection","date":"2023-04-11","arxiv_id":"2304.05015","repositories_listed":0,"syntology":null},{"url":null,"slug":"satr-zero-shot-semantic-segmentation-of-3d","title":"SATR: Zero-Shot Semantic Segmentation of 3D Shapes","date":"2023-04-11","arxiv_id":"2304.04909","repositories_listed":0,"syntology":null},{"url":null,"slug":"ads-unet-a-nested-unet-for-histopathology","title":"ADS_UNet: A Nested UNet for Histopathology Image Segmentation","date":"2023-04-10","arxiv_id":"2304.04567","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-visual-recognition-models-robust-to-image","title":"Are Visual Recognition Models Robust to Image Compression?","date":"2023-04-10","arxiv_id":"2304.04518","repositories_listed":0,"syntology":null},{"url":null,"slug":"hst-mrf-heterogeneous-swin-transformer-with","title":"HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation","date":"2023-04-10","arxiv_id":"2304.04614","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-md-zero-shot-medical-image-segmentation","title":"SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model","date":"2023-04-10","arxiv_id":"2304.05396","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-equivariant-unet-for-histopathology","title":"Scale-Equivariant UNet for Histopathology Image Segmentation","date":"2023-04-10","arxiv_id":"2304.04595","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-with-dual-uncertainty-for-semi","title":"Self-training with dual uncertainty for semi-supervised medical image segmentation","date":"2023-04-10","arxiv_id":"2304.04441","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-kmax-a-simple-unified-approach-for","title":"Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation","date":"2023-04-10","arxiv_id":"2304.04694","repositories_listed":0,"syntology":null},{"url":null,"slug":"foramvit-gan-exploring-new-paradigms-in-deep","title":"ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis","date":"2023-04-09","arxiv_id":"2304.04291","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-dice-loss-gradient-and-the-ways-to","title":"On the dice loss gradient and the ways to mimic it","date":"2023-04-09","arxiv_id":"2304.04319","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-model-sam-for-digital","title":"Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging","date":"2023-04-09","arxiv_id":"2304.04155","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-object","title":"Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos","date":"2023-04-09","arxiv_id":"2304.04325","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-utilization-in-medical-image","title":"Transformer Utilization in Medical Image Segmentation Networks","date":"2023-04-09","arxiv_id":"2304.04225","repositories_listed":0,"syntology":null},{"url":null,"slug":"marginal-thresholding-in-noisy-image","title":"Marginal Thresholding in Noisy Image Segmentation","date":"2023-04-08","arxiv_id":"2304.04116","repositories_listed":0,"syntology":null},{"url":null,"slug":"medgen3d-a-deep-generative-framework-for","title":"MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation","date":"2023-04-08","arxiv_id":"2304.04106","repositories_listed":0,"syntology":null},{"url":null,"slug":"polygonizer-an-auto-regressive-building","title":"Polygonizer: An auto-regressive building delineator","date":"2023-04-08","arxiv_id":"2304.04048","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cross-scale-hierarchical-transformer-with","title":"A Cross-Scale Hierarchical Transformer with Correspondence-Augmented Attention for inferring Bird's-Eye-View Semantic Segmentation","date":"2023-04-07","arxiv_id":"2304.03650","repositories_listed":0,"syntology":null},{"url":null,"slug":"pallet-detection-from-synthetic-data-using","title":"Pallet Detection from Synthetic Data Using Game Engines","date":"2023-04-07","arxiv_id":"2304.03602","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-neural-eigenfunctions-for","title":"Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation","date":"2023-04-06","arxiv_id":"2304.02841","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-region-contrast-for-enhancing-self","title":"Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation","date":"2023-04-06","arxiv_id":"2304.03406","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-aware-batch-normalization-for-improving","title":"Patch-aware Batch Normalization for Improving Cross-domain Robustness","date":"2023-04-06","arxiv_id":"2304.02848","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-generalization-with-adversarial-1","title":"Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation","date":"2023-04-05","arxiv_id":"2304.02720","repositories_listed":0,"syntology":null}],"record_sha256":"59587e30726bfcd3c00b8bffde68e32afd45f058eeb7a5a4e37f7918498c9aff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}