{"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/96","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":96,"pages_in_order":148,"rows_per_page":100,"rows":[9501,9600],"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/95","next":"/task/semantic-segmentation/papers/97","papers":[{"url":null,"slug":"sea-ice-extraction-via-remote-sensed-imagery","title":"Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges","date":"2023-06-01","arxiv_id":"2306.00303","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-late-blight-disease-in-tomato","title":"Detection of Late Blight Disease in Tomato Leaf Using Image Processing Techniques","date":"2023-05-31","arxiv_id":"2306.06080","repositories_listed":0,"syntology":null},{"url":null,"slug":"rasp-relation-aware-semantic-prior-for-weakly","title":"RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation","date":"2023-05-31","arxiv_id":"2305.19879","repositories_listed":0,"syntology":null},{"url":null,"slug":"cones-concept-embedding-search-for-parameter","title":"ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models","date":"2023-05-30","arxiv_id":"2305.18993","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-adversarial-transfer-learning","title":"Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity","date":"2023-05-30","arxiv_id":"2305.19069","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-adversarial-transfer-learning-1","title":"Multi-source adversarial transfer learning based on similar source domains with local features","date":"2023-05-30","arxiv_id":"2305.19067","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-tuning-of-transformer-models-for","title":"Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer","date":"2023-05-30","arxiv_id":"2305.18948","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-aware-super-resolution-network-with","title":"Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images","date":"2023-05-30","arxiv_id":"2305.19063","repositories_listed":0,"syntology":null},{"url":null,"slug":"truedeep-a-systematic-approach-of-crack","title":"TrueDeep: A systematic approach of crack detection with less data","date":"2023-05-30","arxiv_id":"2305.19088","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-model-based-zero-shot-pose-estimation","title":"ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes","date":"2023-05-29","arxiv_id":"2305.17934","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-mechanisms-in-medical-image","title":"Attention Mechanisms in Medical Image Segmentation: A Survey","date":"2023-05-29","arxiv_id":"2305.17937","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-based-recursive-dynamic","title":"Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining","date":"2023-05-29","arxiv_id":"2305.18092","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-body-shape-classification-based-on-a","title":"Human Body Shape Classification Based on a Single Image","date":"2023-05-29","arxiv_id":"2305.18480","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-with-bidirectional","title":"Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR","date":"2023-05-28","arxiv_id":"2305.18419","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-deep-learning-for-medical-image","title":"Trustworthy Deep Learning for Medical Image Segmentation","date":"2023-05-27","arxiv_id":"2305.17456","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-object-detection-in-document-images","title":"Linear Object Detection in Document Images using Multiple Object Tracking","date":"2023-05-26","arxiv_id":"2305.16968","repositories_listed":0,"syntology":null},{"url":null,"slug":"maskomaly-zero-shot-mask-anomaly-segmentation","title":"Maskomaly:Zero-Shot Mask Anomaly Segmentation","date":"2023-05-26","arxiv_id":"2305.16972","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-distortion-theory-in-coding-for-machines","title":"Rate-Distortion Theory in Coding for Machines and its Application","date":"2023-05-26","arxiv_id":"2305.17295","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-supervised-dual-classifiers-for-semi","title":"Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation","date":"2023-05-25","arxiv_id":"2305.16216","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffclip-leveraging-stable-diffusion-for","title":"DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification","date":"2023-05-25","arxiv_id":"2305.15957","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-continual-learning-approach-to","title":"Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments","date":"2023-05-25","arxiv_id":"2305.15700","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-segment-anything-nerf-with","title":"Interactive Segment Anything NeRF with Feature Imitation","date":"2023-05-25","arxiv_id":"2305.16233","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-vision-transformers-truly-shift","title":"Making Vision Transformers Truly Shift-Equivariant","date":"2023-05-25","arxiv_id":"2305.16316","repositories_listed":0,"syntology":null},{"url":null,"slug":"pearl-preprocessing-enhanced-adversarial","title":"PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation","date":"2023-05-25","arxiv_id":"2305.15709","repositories_listed":0,"syntology":null},{"url":null,"slug":"autodepthnet-high-frame-rate-depth-map","title":"AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras","date":"2023-05-24","arxiv_id":"2305.14731","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-bio-medical-image","title":"Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning","date":"2023-05-24","arxiv_id":"2305.14841","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-path-transformer-with-partition","title":"Dual Path Transformer with Partition Attention","date":"2023-05-24","arxiv_id":"2305.14768","repositories_listed":0,"syntology":null},{"url":null,"slug":"gtnet-graph-transformer-network-for-3d-point","title":"GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation","date":"2023-05-24","arxiv_id":"2305.15213","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmnet-multi-mask-network-for-referring-image","title":"MMNet: Multi-Mask Network for Referring Image Segmentation","date":"2023-05-24","arxiv_id":"2305.14969","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-mutual-attention-and-iterative","title":"Multi-Modal Mutual Attention and Iterative Interaction for Referring Image Segmentation","date":"2023-05-24","arxiv_id":"2305.15302","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-based-uncertainty-estimation-for-an","title":"Sampling-based Uncertainty Estimation for an Instance Segmentation Network","date":"2023-05-24","arxiv_id":"2305.14977","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-by-semantic-proportions","title":"Semantic Segmentation by Semantic Proportions","date":"2023-05-24","arxiv_id":"2305.15608","repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-object-detection-on-fisheye-cameras","title":"Streaming Object Detection on Fisheye Cameras for Automatic Parking","date":"2023-05-24","arxiv_id":"2305.14713","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixup-privacy-a-simple-yet-effective-approach","title":"Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation","date":"2023-05-23","arxiv_id":"2305.13756","repositories_listed":0,"syntology":null},{"url":null,"slug":"siamese-masked-autoencoders","title":"Siamese Masked Autoencoders","date":"2023-05-23","arxiv_id":"2305.14344","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-free-domain-adaptation-for-rgb-d","title":"Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers","date":"2023-05-23","arxiv_id":"2305.14269","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-vector-machine-guided-reproducing","title":"Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures","date":"2023-05-23","arxiv_id":"2305.16402","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-semantics-matters-a-deep-study-on","title":"Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map","date":"2023-05-23","arxiv_id":"2305.14038","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualising-implicit-representations-for","title":"Contextualising Implicit Representations for Semantic Tasks","date":"2023-05-22","arxiv_id":"2305.13312","repositories_listed":0,"syntology":null},{"url":"/paper/hi-resnet-a-high-resolution-remote-sensing","slug":"hi-resnet-a-high-resolution-remote-sensing","title":"Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation","date":"2023-05-22","arxiv_id":"2305.12691","repositories_listed":0,"syntology":null},{"url":null,"slug":"materialistic-selecting-similar-materials-in","title":"Materialistic: Selecting Similar Materials in Images","date":"2023-05-22","arxiv_id":"2305.13291","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-guided-context-modeling-for-indoor","title":"Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition","date":"2023-05-22","arxiv_id":"2305.12661","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-radar-detections","title":"Semantic Segmentation of Radar Detections using Convolutions on Point Clouds","date":"2023-05-22","arxiv_id":"2305.12775","repositories_listed":0,"syntology":null},{"url":null,"slug":"coronary-artery-semantic-labeling-using-edge","title":"Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network","date":"2023-05-21","arxiv_id":"2305.12327","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-noc-adversarial-cam-generation-for-weakly","title":"P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors","date":"2023-05-21","arxiv_id":"2305.12522","repositories_listed":0,"syntology":null},{"url":null,"slug":"vl-fields-towards-language-grounded-neural","title":"VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations","date":"2023-05-21","arxiv_id":"2305.12427","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-vlgm-bi-level-class-severity-aware-vision","title":"Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation","date":"2023-05-20","arxiv_id":"2305.12231","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quality-assurance-framework-for-real-time","title":"A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy","date":"2023-05-19","arxiv_id":"2305.11715","repositories_listed":0,"syntology":null},{"url":null,"slug":"cm-masksd-cross-modality-masked-self","title":"CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation","date":"2023-05-19","arxiv_id":"2305.11481","repositories_listed":0,"syntology":null},{"url":null,"slug":"image2ssm-reimagining-statistical-shape","title":"Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions","date":"2023-05-19","arxiv_id":"2305.11946","repositories_listed":0,"syntology":null},{"url":null,"slug":"joinedtrans-prior-guided-multi-task","title":"JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection","date":"2023-05-19","arxiv_id":"2305.11504","repositories_listed":0,"syntology":null},{"url":null,"slug":"sim-to-real-segmentation-in-robot-assisted","title":"Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs Towards Robot-assisted Intubation","date":"2023-05-19","arxiv_id":"2305.11686","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-subabdominal-mri-image-segmentation","title":"An image segmentation algorithm based on multi-scale feature pyramid network","date":"2023-05-18","arxiv_id":"2305.10631","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-incremental-few-shot-semantic","title":"Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation","date":"2023-05-18","arxiv_id":"2305.10868","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-free-audio-visual-segmentation","title":"Annotation-free Audio-Visual Segmentation","date":"2023-05-18","arxiv_id":"2305.11019","repositories_listed":0,"syntology":null},{"url":null,"slug":"sdc-uda-volumetric-unsupervised-domain-1","title":"SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation","date":"2023-05-18","arxiv_id":"2305.11012","repositories_listed":0,"syntology":null},{"url":null,"slug":"slotdiffusion-object-centric-generative-1","title":"SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models","date":"2023-05-18","arxiv_id":"2305.11281","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-concealed-object","title":"Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping","date":"2023-05-18","arxiv_id":"2305.11003","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-multiple-sources-knowledge-for","title":"Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images","date":"2023-05-17","arxiv_id":"2305.09893","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-for-poultry-science","title":"SAM for Poultry Science","date":"2023-05-17","arxiv_id":"2305.10254","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-graph-neural-networks-for-moving","title":"Inductive Graph Neural Networks for Moving Object Segmentation","date":"2023-05-16","arxiv_id":"2305.09585","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-online-testing-of-deep-neural","title":"One-Shot Online Testing of Deep Neural Networks Based on Distribution Shift Detection","date":"2023-05-16","arxiv_id":"2305.09348","repositories_listed":0,"syntology":null},{"url":null,"slug":"panelnet-understanding-360-indoor-environment","title":"PanelNet: Understanding 360 Indoor Environment via Panel Representation","date":"2023-05-16","arxiv_id":"2305.09078","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-semantic-geometric-approach-for","title":"A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds","date":"2023-05-15","arxiv_id":"2305.15420","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-in-the-loop-functionalizing-fold","title":"AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines","date":"2023-05-15","arxiv_id":"2305.09031","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-performance-of-vision-transformers","title":"Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation","date":"2023-05-15","arxiv_id":"2305.08551","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-submodular-function-maximization","title":"Fast Submodular Function Maximization","date":"2023-05-15","arxiv_id":"2305.08367","repositories_listed":0,"syntology":null},{"url":null,"slug":"m-6-doc-a-large-scale-multi-format-multi-type","title":"M$^{6}$Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis","date":"2023-05-15","arxiv_id":"2305.08719","repositories_listed":0,"syntology":null},{"url":null,"slug":"scrnet-a-retinex-structure-based-low-light","title":"SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency","date":"2023-05-14","arxiv_id":"2305.08053","repositories_listed":0,"syntology":null},{"url":null,"slug":"aura-automatic-mask-generator-using","title":"AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal","date":"2023-05-13","arxiv_id":"2305.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-via-probabilistic-graph","title":"Image Segmentation via Probabilistic Graph Matching","date":"2023-05-13","arxiv_id":"2305.07954","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-unlearned-feature-for-brain","title":"Learning to Learn Unlearned Feature for Brain Tumor Segmentation","date":"2023-05-13","arxiv_id":"2305.08878","repositories_listed":0,"syntology":null},{"url":null,"slug":"metamorphosis-task-oriented-privacy-cognizant","title":"MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning","date":"2023-05-13","arxiv_id":"2305.07815","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizable-medical-image","title":"Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation","date":"2023-05-13","arxiv_id":"2305.07883","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-grain-boundary-gb-segmentation-and","title":"Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy","date":"2023-05-12","arxiv_id":"2305.07790","repositories_listed":0,"syntology":null},{"url":null,"slug":"hear-to-segment-unmixing-the-audio-to-guide","title":"Transavs: End-To-End Audio-Visual Segmentation With Transformer","date":"2023-05-12","arxiv_id":"2305.07223","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-with-segment-anything","title":"Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping","date":"2023-05-12","arxiv_id":"2305.07586","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-segmentation-of-heterogeneous","title":"Multi-Class Segmentation of Heterogeneous Areas in Biomedical and Environmental Images Based on the Assessment of Local Edge Density","date":"2023-05-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"roi-based-deep-image-compression-with-swin","title":"ROI-based Deep Image Compression with Swin Transformers","date":"2023-05-12","arxiv_id":"2305.07783","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-rarely-learn","title":"Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation","date":"2023-05-11","arxiv_id":"2305.06568","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-hallucinator-towards-few-shot-cross","title":"Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation","date":"2023-05-11","arxiv_id":"2305.06978","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-better-understanding-of-the-4","title":"Towards a Better Understanding of the Computer Vision Research Community in Africa","date":"2023-05-11","arxiv_id":"2305.06773","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-the-robustness-of-the","title":"An Empirical Study on the Robustness of the Segment Anything Model (SAM)","date":"2023-05-10","arxiv_id":"2305.06422","repositories_listed":0,"syntology":null},{"url":null,"slug":"dmnr-unsupervised-de-noising-of-point-clouds","title":"DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles","date":"2023-05-10","arxiv_id":"2305.05991","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-for-improved-lossless","title":"Image Segmentation For Improved Lossless Screen Content Compression","date":"2023-05-10","arxiv_id":"2305.05996","repositories_listed":0,"syntology":null},{"url":null,"slug":"radious-unveiling-the-enigma-of-dental","title":"Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation","date":"2023-05-10","arxiv_id":"2305.06236","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-instance-segmentation-by","title":"Self-Supervised Instance Segmentation by Grasping","date":"2023-05-10","arxiv_id":"2305.06305","repositories_listed":0,"syntology":null},{"url":null,"slug":"vtpnet-for-3d-deep-learning-on-point-cloud","title":"VTPNet for 3D deep learning on point cloud","date":"2023-05-10","arxiv_id":"2305.06115","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-semantic-5","title":"Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching","date":"2023-05-09","arxiv_id":"2305.05789","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-light-diffusion-for-portraits","title":"Controllable Light Diffusion for Portraits","date":"2023-05-08","arxiv_id":"2305.04745","repositories_listed":0,"syntology":null},{"url":null,"slug":"osta-one-shot-task-adaptive-channel-selection","title":"OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images","date":"2023-05-08","arxiv_id":"2305.04766","repositories_listed":0,"syntology":null},{"url":null,"slug":"living-in-a-material-world-learning-material","title":"Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation","date":"2023-05-07","arxiv_id":"2305.04334","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-the-veterinary-cytological","title":"Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis","date":"2023-05-07","arxiv_id":"2305.04332","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-what-you-need-enhancing-segmentation","title":"Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting","date":"2023-05-06","arxiv_id":"2305.03902","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-and-statistical-texture-knowledge-1","title":"Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation","date":"2023-05-06","arxiv_id":"2305.03944","repositories_listed":0,"syntology":null},{"url":null,"slug":"white-matter-hyperintensities-segmentation","title":"White Matter Hyperintensities Segmentation Using Probabilistic TransUNet","date":"2023-05-06","arxiv_id":"2305.03912","repositories_listed":0,"syntology":null},{"url":null,"slug":"badsam-exploring-security-vulnerabilities-of","title":"BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks","date":"2023-05-05","arxiv_id":"2305.03289","repositories_listed":0,"syntology":null},{"url":null,"slug":"clothes-grasping-and-unfolding-based-on-rgb-d","title":"Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation","date":"2023-05-05","arxiv_id":"2305.03259","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-segment-anything-model-sam-boost-medical","title":"Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey","date":"2023-05-05","arxiv_id":"2305.03678","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-fundus-vascular-images-based","title":"MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation","date":"2023-05-05","arxiv_id":"2305.03617","repositories_listed":0,"syntology":null}],"record_sha256":"4aa1f5b15543c5fce3c9ba9811f82502ffaea222e0120c8aef141158f3c77f1d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}