{"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/142","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":142,"pages_in_order":148,"rows_per_page":100,"rows":[14101,14200],"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/141","next":"/task/semantic-segmentation/papers/143","papers":[{"url":null,"slug":"sports-field-localization-via-deep-structured","title":"Sports Field Localization via Deep Structured Models","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ubernet-training-a-universal-convolutional-1","title":"Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"webly-supervised-semantic-segmentation","title":"Webly Supervised Semantic Segmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-free-video-object-segmentation","title":"Flow-free Video Object Segmentation","date":"2017-06-29","arxiv_id":"1706.09544","repositories_listed":0,"syntology":null},{"url":"/paper/online-adaptation-of-convolutional-neural","slug":"online-adaptation-of-convolutional-neural","title":"Online Adaptation of Convolutional Neural Networks for Video Object Segmentation","date":"2017-06-28","arxiv_id":"1706.09364","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantically-informed-multiview-surface","title":"Semantically Informed Multiview Surface Refinement","date":"2017-06-26","arxiv_id":"1706.08336","repositories_listed":0,"syntology":null},{"url":null,"slug":"irregular-convolutional-neural-networks","title":"Irregular Convolutional Neural Networks","date":"2017-06-24","arxiv_id":"1706.07966","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computer-vision-pipeline-for-automated","title":"A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography","date":"2017-06-22","arxiv_id":"1706.07342","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enhanced-deep-convolutional-encoder","title":"An Enhanced Deep Convolutional Encoder-Decoder Network for Road Segmentation on Aerial Imagery","date":"2017-06-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"satellite-imagery-feature-detection-using","title":"Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition","date":"2017-06-19","arxiv_id":"1706.06169","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convolutional-autoencoder-approach-for","title":"A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation","date":"2017-06-15","arxiv_id":"1706.04970","repositories_listed":0,"syntology":null},{"url":null,"slug":"suggestive-annotation-a-deep-active-learning","title":"Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation","date":"2017-06-15","arxiv_id":"1706.04737","repositories_listed":0,"syntology":null},{"url":null,"slug":"-net-deep-learning-for-generalized","title":"$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters","date":"2017-06-14","arxiv_id":"1706.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-shape-descriptors-from-part","title":"Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks","date":"2017-06-14","arxiv_id":"1706.04496","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamic-graph-cuts-method-with-integrated","title":"A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images","date":"2017-06-11","arxiv_id":"1706.03372","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-obstacle-detection-in","title":"Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields","date":"2017-06-09","arxiv_id":"1706.02908","repositories_listed":0,"syntology":null},{"url":null,"slug":"biseg-simultaneous-instance-segmentation-and","title":"BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks","date":"2017-06-07","arxiv_id":"1706.02135","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-network-built-in-priors-in","title":"Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation","date":"2017-06-06","arxiv_id":"1706.02189","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-deep-and-shallow-networks-for","title":"Integrated Deep and Shallow Networks for Salient Object Detection","date":"2017-06-02","arxiv_id":"1706.00530","repositories_listed":0,"syntology":null},{"url":null,"slug":"line-profile-based-segmentation-algorithm-for","title":"Line Profile Based Segmentation Algorithm for Touching Corn Kernels","date":"2017-06-01","arxiv_id":"1706.00396","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-semantic-segmentation-based","title":"Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation","date":"2017-05-25","arxiv_id":"1705.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-with-semantic-priors","title":"Salient Object Detection with Semantic Priors","date":"2017-05-23","arxiv_id":"1705.08207","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-multi-object-segmentation-with-novel","title":"Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors","date":"2017-05-22","arxiv_id":"1705.10311","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-depth-into-both-cnn-and-crf-for","title":"Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation","date":"2017-05-21","arxiv_id":"1705.07383","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-level-set-method-for-image","title":"A deep level set method for image segmentation","date":"2017-05-17","arxiv_id":"1705.06260","repositories_listed":0,"syntology":null},{"url":null,"slug":"cardiacnet-segmentation-of-left-atrium-and","title":"CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN","date":"2017-05-17","arxiv_id":"1705.06333","repositories_listed":0,"syntology":null},{"url":null,"slug":"wordfence-text-detection-in-natural-images","title":"WordFence: Text Detection in Natural Images with Border Awareness","date":"2017-05-15","arxiv_id":"1705.05483","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-continuous-admm-for-transductive","title":"Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs","date":"2017-05-14","arxiv_id":"1705.05020","repositories_listed":0,"syntology":null},{"url":null,"slug":"combination-of-hidden-markov-random-field-and","title":"Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation","date":"2017-05-13","arxiv_id":"1705.04823","repositories_listed":0,"syntology":null},{"url":null,"slug":"parametric-imaging-of-fdg-pet-data-using","title":"Parametric Imaging of FDG-PET Data Using Physiology and Iterative Regularization: Application to the Hepatic and Renal Systems","date":"2017-05-11","arxiv_id":"1705.04603","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-brain-tumor-detection-and","title":"Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks","date":"2017-05-10","arxiv_id":"1705.03820","repositories_listed":0,"syntology":null},{"url":null,"slug":"derivate-based-component-trees-for-multi","title":"Derivate-based Component-Trees for Multi-Channel Image Segmentation","date":"2017-05-04","arxiv_id":"1705.01906","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-segment-with-image-level","title":"Learning to segment with image-level supervision","date":"2017-05-03","arxiv_id":"1705.01262","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-corrective-boosting-for-learning-fully","title":"Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data","date":"2017-05-02","arxiv_id":"1705.00938","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-foreground-object-ambiguity-and","title":"Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)","date":"2017-04-30","arxiv_id":"1705.00366","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-real-time-background-cut-for","title":"Automatic Real-time Background Cut for Portrait Videos","date":"2017-04-28","arxiv_id":"1704.08812","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-discovery-via-cohesion-measurement","title":"Object Discovery via Cohesion Measurement","date":"2017-04-28","arxiv_id":"1704.08944","repositories_listed":0,"syntology":null},{"url":"/paper/improving-facial-attribute-prediction-using","slug":"improving-facial-attribute-prediction-using","title":"Improving Facial Attribute Prediction using Semantic Segmentation","date":"2017-04-27","arxiv_id":"1704.08740","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-region-force-for-variational-models-in","title":"New region force for variational models in image segmentation and high dimensional data clustering","date":"2017-04-26","arxiv_id":"1704.08218","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-instance-segmentation-with-object","title":"Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition","date":"2017-04-24","arxiv_id":"1704.07402","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-medical-image-processing","title":"Deep Learning for Medical Image Processing: Overview, Challenges and Future","date":"2017-04-22","arxiv_id":"1704.06825","repositories_listed":0,"syntology":null},{"url":"/paper/learning-video-object-segmentation-with","slug":"learning-video-object-segmentation-with","title":"Learning Video Object Segmentation with Visual Memory","date":"2017-04-19","arxiv_id":"1704.05737","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-adversarial-perturbations-against","title":"Universal Adversarial Perturbations Against Semantic Image Segmentation","date":"2017-04-19","arxiv_id":"1704.05712","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-object-segmentation-in-video-by","title":"Unsupervised object segmentation in video by efficient selection of highly probable positive features","date":"2017-04-19","arxiv_id":"1704.05674","repositories_listed":0,"syntology":null},{"url":"/paper/instance-level-salient-object-segmentation","slug":"instance-level-salient-object-segmentation","title":"Instance-Level Salient Object Segmentation","date":"2017-04-12","arxiv_id":"1704.03604","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-multi-task-medical-image","title":"Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities","date":"2017-04-11","arxiv_id":"1704.03379","repositories_listed":0,"syntology":null},{"url":null,"slug":"loss-max-pooling-for-semantic-image","title":"Loss Max-Pooling for Semantic Image Segmentation","date":"2017-04-10","arxiv_id":"1704.02966","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-where-to-look-data-driven-viewpoint","title":"Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes","date":"2017-04-07","arxiv_id":"1704.02393","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantically-guided-video-object-segmentation","title":"Semantically-Guided Video Object Segmentation","date":"2017-04-06","arxiv_id":"1704.01926","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-breast-ultrasound-image","title":"Automatic Breast Ultrasound Image Segmentation: A Survey","date":"2017-04-04","arxiv_id":"1704.01472","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-proofreading-of-automatic","title":"Guided Proofreading of Automatic Segmentations for Connectomics","date":"2017-04-04","arxiv_id":"1704.00848","repositories_listed":0,"syntology":null},{"url":null,"slug":"pose2instance-harnessing-keypoints-for-person","title":"Pose2Instance: Harnessing Keypoints for Person Instance Segmentation","date":"2017-04-04","arxiv_id":"1704.01152","repositories_listed":0,"syntology":null},{"url":"/paper/the-2017-davis-challenge-on-video-object","slug":"the-2017-davis-challenge-on-video-object","title":"The 2017 DAVIS Challenge on Video Object Segmentation","date":"2017-04-03","arxiv_id":"1704.00675","repositories_listed":0,"syntology":null},{"url":null,"slug":"configurable-3d-scene-synthesis-and-2d-image","title":"Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars","date":"2017-04-01","arxiv_id":"1704.00112","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-recognize-animals-by-watching","title":"Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-labelled-dataset-construction","title":"Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis","date":"2017-03-30","arxiv_id":"1703.10571","repositories_listed":0,"syntology":null},{"url":null,"slug":"planecell-representing-the-3d-space-with","title":"Planecell: Representing the 3D Space with Planes","date":"2017-03-30","arxiv_id":"1703.10304","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-classifiers-for-image","title":"Evaluation of Classifiers for Image Segmentation: Applications for Eucalypt Forest Inventory","date":"2017-03-28","arxiv_id":"1703.09436","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-and-weakly-supervised-semantic","title":"Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network","date":"2017-03-28","arxiv_id":"1703.09695","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-deep-learning-for-consistent","title":"Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras","date":"2017-03-26","arxiv_id":"1703.08866","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-learning-of-tree-potentials-in-crf","title":"Structured Learning of Tree Potentials in CRF for Image Segmentation","date":"2017-03-26","arxiv_id":"1703.08764","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-nonconvex-splitting-method-for-symmetric","title":"A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality","date":"2017-03-24","arxiv_id":"1703.08267","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-region-mining-with-adversarial-erasing","title":"Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach","date":"2017-03-24","arxiv_id":"1703.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-corrective-perturbations-for-semantic","title":"Self corrective Perturbations for Semantic Segmentation and Classification","date":"2017-03-23","arxiv_id":"1703.07928","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-different-methods-for-tissue","title":"Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images","date":"2017-03-17","arxiv_id":"1703.05990","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-contrast-invariant-l1-data","title":"Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition","date":"2017-03-16","arxiv_id":"1703.05560","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-the-deep-learning-based","title":"Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project","date":"2017-03-15","arxiv_id":"1703.04967","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-networks-to-detect","title":"Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features","date":"2017-03-14","arxiv_id":"1703.04559","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-learning-in-the-presence-of-sparse","title":"Subspace Learning in The Presence of Sparse Structured Outliers and Noise","date":"2017-03-14","arxiv_id":"1703.04611","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-lidar-based-road-detection-using-fully","title":"Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks","date":"2017-03-10","arxiv_id":"1703.03613","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-semantic-face-segmentation-with","title":"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks","date":"2017-03-09","arxiv_id":"1703.03305","repositories_listed":0,"syntology":null},{"url":"/paper/fast-and-robust-detection-of-fallen-people","slug":"fast-and-robust-detection-of-fallen-people","title":"Fast and Robust Detection of Fallen People from a Mobile Robot","date":"2017-03-09","arxiv_id":"1703.03349","repositories_listed":0,"syntology":null},{"url":null,"slug":"lesionseg-semantic-segmentation-of-skin","title":"LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network","date":"2017-03-09","arxiv_id":"1703.03372","repositories_listed":0,"syntology":null},{"url":null,"slug":"prior-based-hierarchical-segmentation","title":"Prior-based Hierarchical Segmentation Highlighting Structures of Interest","date":"2017-03-09","arxiv_id":"1703.03196","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-white-matter-bundle-segmentation-using","title":"Direct White Matter Bundle Segmentation using Stacked U-Nets","date":"2017-03-06","arxiv_id":"1703.02036","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-multispectral-dataset-for","title":"High-Resolution Multispectral Dataset for Semantic Segmentation","date":"2017-03-06","arxiv_id":"1703.01918","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-examples-for-semantic-image","title":"Adversarial Examples for Semantic Image Segmentation","date":"2017-03-03","arxiv_id":"1703.01101","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-flow-based-online-multiple-object","title":"Instance Flow Based Online Multiple Object Tracking","date":"2017-03-03","arxiv_id":"1703.01289","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-refinement-network-for-coarse-to-fine","title":"Label Refinement Network for Coarse-to-Fine Semantic Segmentation","date":"2017-03-01","arxiv_id":"1703.00551","repositories_listed":0,"syntology":null},{"url":null,"slug":"perturb-and-mpm-quantifying-segmentation","title":"Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs","date":"2017-03-01","arxiv_id":"1703.00312","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-shape-segmentation-via-shape-fully","title":"3D Shape Segmentation via Shape Fully Convolutional Networks","date":"2017-02-28","arxiv_id":"1702.08675","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-networks-for-the-detection-of","title":"Adversarial Networks for the Detection of Aggressive Prostate Cancer","date":"2017-02-26","arxiv_id":"1702.08014","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-modular-cnn-architectures-for-joint","title":"Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation","date":"2017-02-26","arxiv_id":"1702.08009","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-means-clustering-and-ensemble-of","title":"k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge","date":"2017-02-23","arxiv_id":"1702.07333","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-fully-automated-segmentation-of","title":"Robust and fully automated segmentation of mandible from CT scans","date":"2017-02-23","arxiv_id":"1702.07059","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-sourcing-image-segmentation-with","title":"Crowd Sourcing Image Segmentation with iaSTAPLE","date":"2017-02-21","arxiv_id":"1702.06461","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressively-diffused-networks-for-semantic","title":"Progressively Diffused Networks for Semantic Image Segmentation","date":"2017-02-20","arxiv_id":"1702.05839","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-graph-construction-for-fast-image","title":"Revisiting Graph Construction for Fast Image Segmentation","date":"2017-02-18","arxiv_id":"1702.05650","repositories_listed":0,"syntology":null},{"url":"/paper/the-ciona17-dataset-for-semantic-segmentation","slug":"the-ciona17-dataset-for-semantic-segmentation","title":"The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment","date":"2017-02-18","arxiv_id":"1702.05564","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-cell-nuclei-segmentation-with-balanced","title":"3D Cell Nuclei Segmentation with Balanced Graph Partitioning","date":"2017-02-17","arxiv_id":"1702.05413","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-visual-applications-a","title":"Domain Adaptation for Visual Applications: A Comprehensive Survey","date":"2017-02-17","arxiv_id":"1702.05374","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-normalized-inputs-for-iterative","title":"Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation","date":"2017-02-16","arxiv_id":"1702.05174","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-model-integrating-fcnns-and","title":"A deep learning model integrating FCNNs and CRFs for brain tumor segmentation","date":"2017-02-15","arxiv_id":"1702.04528","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-decomposition-framework-for","title":"An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses","date":"2017-02-13","arxiv_id":"1702.03690","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adversarial-regularisation-for-semi","title":"An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks","date":"2017-02-08","arxiv_id":"1702.02382","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeded-laplaican-an-eigenfunction-solution","title":"Seeded Laplaican: An Eigenfunction Solution for Scribble Based Interactive Image Segmentation","date":"2017-02-03","arxiv_id":"1702.00882","repositories_listed":0,"syntology":null},{"url":"/paper/exploiting-saliency-for-object-segmentation","slug":"exploiting-saliency-for-object-segmentation","title":"Exploiting saliency for object segmentation from image level labels","date":"2017-01-28","arxiv_id":"1701.08261","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-level-region-consistency-with","title":"Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation","date":"2017-01-25","arxiv_id":"1701.07122","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-projected-gradient-descent-method-for-crf","title":"A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials","date":"2017-01-24","arxiv_id":"1701.06805","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-group-orthogonal-neural-networks","title":"Training Group Orthogonal Neural Networks with Privileged Information","date":"2017-01-24","arxiv_id":"1701.06772","repositories_listed":0,"syntology":null}],"record_sha256":"172bf9f60b62285106ba8e7466d27ff878d132c912a039eb5690f437ca2f7da7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}