{"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/117","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":117,"pages_in_order":131,"rows_per_page":100,"rows":[11601,11700],"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/116","next":"/task/segmentation/papers/118","papers":[{"url":null,"slug":"an-adversarial-learning-approach-to-medical","title":"An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection","date":"2018-10-25","arxiv_id":"1810.10850","repositories_listed":0,"syntology":null},{"url":null,"slug":"aunet-attention-guided-dense-upsampling","title":"AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms","date":"2018-10-24","arxiv_id":"1810.10151","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-evaluation-of-semantic-segmentation","title":"Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving","date":"2018-10-24","arxiv_id":"1810.10193","repositories_listed":0,"syntology":null},{"url":null,"slug":"coarse-to-fine-volumetric-segmentation-of","title":"Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT","date":"2018-10-24","arxiv_id":"1810.10293","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-color-space-adaptation-from","title":"Learning color space adaptation from synthetic to real images of cirrus clouds","date":"2018-10-24","arxiv_id":"1810.10286","repositories_listed":0,"syntology":null},{"url":null,"slug":"mask-propagation-network-for-video-object","title":"Mask Propagation Network for Video Object Segmentation","date":"2018-10-24","arxiv_id":"1810.10289","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-analysis-in-human-centric-cyber","title":"Segmentation Analysis in Human Centric Cyber-Physical Systems using Graphical Lasso","date":"2018-10-24","arxiv_id":"1810.10533","repositories_listed":0,"syntology":null},{"url":null,"slug":"cereals-cost-effective-region-based-active","title":"CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation","date":"2018-10-23","arxiv_id":"1810.09726","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-network-pruning-to","title":"Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy","date":"2018-10-23","arxiv_id":"1810.09735","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-diagnosis-and-segmentation","title":"End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging","date":"2018-10-23","arxiv_id":"1810.10117","repositories_listed":0,"syntology":null},{"url":null,"slug":"atrial-fibrosis-quantification-based-on","title":"Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images","date":"2018-10-22","arxiv_id":"1810.09075","repositories_listed":0,"syntology":null},{"url":null,"slug":"left-ventricle-segmentation-via-optical-flow","title":"Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion","date":"2018-10-20","arxiv_id":"1810.08753","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-fast-segmentation-with-teacher","title":"Improving Fast Segmentation With Teacher-student Learning","date":"2018-10-19","arxiv_id":"1810.08476","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-guided-deep-network-for-weakly","title":"Saliency guided deep network for weakly-supervised image segmentation","date":"2018-10-19","arxiv_id":"1810.08378","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-brain-tumor-segmentation-using","title":"Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation","date":"2018-10-18","arxiv_id":"1810.07884","repositories_listed":0,"syntology":null},{"url":null,"slug":"bottleneck-supervised-u-net-for-pixel-wise","title":"Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation","date":"2018-10-16","arxiv_id":"1810.10331","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-preprocessing-to-optimize-watershed","title":"CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images","date":"2018-10-16","arxiv_id":"1810.06933","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-self-guided-dense-annotations-for","title":"Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation","date":"2018-10-16","arxiv_id":"1810.07050","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-extension-to-fuzzy-connectivity-for","title":"A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation","date":"2018-10-14","arxiv_id":"1810.06071","repositories_listed":0,"syntology":null},{"url":null,"slug":"lung-structures-enhancement-in-chest","title":"Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training","date":"2018-10-14","arxiv_id":"1810.05989","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-semantics-in-adversarial-training","title":"Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation","date":"2018-10-13","arxiv_id":"1810.05852","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-domain-adaptation-framework-for","title":"A Novel Domain Adaptation Framework for Medical Image Segmentation","date":"2018-10-11","arxiv_id":"1810.05732","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-adversarial-examples-based-on","title":"Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation","date":"2018-10-11","arxiv_id":"1810.05162","repositories_listed":0,"syntology":null},{"url":null,"slug":"infinet-fully-convolutional-networks-for","title":"InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation","date":"2018-10-11","arxiv_id":"1810.05735","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-level-set-for-segmenting-brain","title":"Deep Recurrent Level Set for Segmenting Brain Tumors","date":"2018-10-10","arxiv_id":"1810.04752","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-multi-modal-sensors-fusion-system","title":"End-To-End multi-modal sensors fusion system for urban automated driving","date":"2018-10-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-deep-representations-for-semantic","title":"Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview","date":"2018-10-10","arxiv_id":"1810.04377","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-institutional-deep-learning-modeling","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","date":"2018-10-10","arxiv_id":"1810.04304","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-generative-refinement-adversarial","title":"Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation","date":"2018-10-09","arxiv_id":"1810.03871","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-using-unsupervised","title":"Image Segmentation using Unsupervised Watershed Algorithm with an Over-segmentation Reduction Technique","date":"2018-10-09","arxiv_id":"1810.03908","repositories_listed":0,"syntology":null},{"url":null,"slug":"uolo-automatic-object-detection-and","title":"UOLO - automatic object detection and segmentation in biomedical images","date":"2018-10-09","arxiv_id":"1810.05729","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-6d-object-pose-estimation-in-cluttered","title":"Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks","date":"2018-10-08","arxiv_id":"1810.03410","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-geodesic-learning-for-segmentation-and","title":"Deep Geodesic Learning for Segmentation and Anatomical Landmarking","date":"2018-10-06","arxiv_id":"1810.04021","repositories_listed":0,"syntology":null},{"url":"/paper/dark-model-adaptation-semantic-image","slug":"dark-model-adaptation-semantic-image","title":"Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime","date":"2018-10-05","arxiv_id":"1810.02575","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-segmentation-for-classical-chinese","title":"Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding","date":"2018-10-05","arxiv_id":"1810.03479","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-segmentation-of-anatomical","title":"Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder","date":"2018-10-04","arxiv_id":"1810.02113","repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-augmentation-can-deep-learning-based","title":"Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?","date":"2018-10-03","arxiv_id":"1810.01621","repositories_listed":0,"syntology":null},{"url":null,"slug":"know-what-your-neighbors-do-3d-semantic","title":"Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds","date":"2018-10-02","arxiv_id":"1810.01151","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-segment-inputs-for-nmt-favors","title":"Learning to Segment Inputs for NMT Favors Character-Level Processing","date":"2018-10-02","arxiv_id":"1810.01480","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatically-tailoring-unsupervised","title":"Automatically Tailoring Unsupervised Morphological Segmentation to the Language","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rgb-d-object-detection-and-semantic","title":"RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter","date":"2018-10-01","arxiv_id":"1810.00818","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-slt-interactions-parsing-system-at-the","title":"The SLT-Interactions Parsing System at the CoNLL 2018 Shared Task","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-trajectory-segmentation-and","title":"Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations","date":"2018-10-01","arxiv_id":"1810.00599","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-end-to-end-atrial","title":"Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation","date":"2018-09-30","arxiv_id":"1810.00475","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnns-fusion-for-building-detection-in-aerial","title":"CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge","date":"2018-09-28","arxiv_id":"1809.10976","repositories_listed":0,"syntology":null},{"url":"/paper/panoptic-segmentation-with-an-end-to-end-cell","slug":"panoptic-segmentation-with-an-end-to-end-cell","title":"Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis","date":"2018-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-for-urban-planning-maps","title":"Semantic Segmentation for Urban Planning Maps based on U-Net","date":"2018-09-28","arxiv_id":"1809.10862","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-networks-for-supervised","title":"Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diagnostics-in-semantic-segmentation","title":"Diagnostics in Semantic Segmentation","date":"2018-09-27","arxiv_id":"1809.10328","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-cloud-detection-and-segmentation","title":"Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS","date":"2018-09-27","arxiv_id":"1809.10801","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-myocardium-segmentation-in-cardiac","title":"Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information","date":"2018-09-27","arxiv_id":"1810.03968","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-increased-trustworthiness-of-deep","title":"Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI","date":"2018-09-27","arxiv_id":"1809.10430","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-coarse-to-fine-framework-for-video-object","title":"A Coarse-To-Fine Framework For Video Object Segmentation","date":"2018-09-26","arxiv_id":"1809.10260","repositories_listed":0,"syntology":null},{"url":null,"slug":"left-ventricle-segmentation-and","title":"Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning","date":"2018-09-26","arxiv_id":"1809.10221","repositories_listed":0,"syntology":null},{"url":"/paper/triply-supervised-decoder-networks-for-joint","slug":"triply-supervised-decoder-networks-for-joint","title":"Triply Supervised Decoder Networks for Joint Detection and Segmentation","date":"2018-09-25","arxiv_id":"1809.09299","repositories_listed":0,"syntology":null},{"url":null,"slug":"cylindrical-transform-3d-semantic","title":"Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images","date":"2018-09-24","arxiv_id":"1809.10245","repositories_listed":0,"syntology":null},{"url":null,"slug":"artistic-instance-aware-image-filtering-by","title":"Artistic Instance-Aware Image Filtering by Convolutional Neural Networks","date":"2018-09-22","arxiv_id":"1809.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"exclusive-independent-probability-estimation","title":"Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation","date":"2018-09-21","arxiv_id":"1809.08168","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-segmentation-using-deep-learning","title":"Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images","date":"2018-09-20","arxiv_id":"1809.07786","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-progress-in-semantic-image","title":"Recent progress in semantic image segmentation","date":"2018-09-20","arxiv_id":"1809.10198","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-fully-convolutional-network-for","title":"Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation","date":"2018-09-19","arxiv_id":"1809.10203","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-segmentation-of-mandible-from","title":"3D segmentation of mandible from multisectional CT scans by convolutional neural networks","date":"2018-09-18","arxiv_id":"1809.06752","repositories_listed":0,"syntology":null},{"url":null,"slug":"dasnet-reducing-pixel-level-annotations-for","title":"DASNet: Reducing Pixel-level Annotations for Instance and Semantic Segmentation","date":"2018-09-17","arxiv_id":"1809.06013","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-convolutional-neural-networks-for","title":"Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation","date":"2018-09-17","arxiv_id":"1809.06191","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-abnormality-detection-for-automatic","title":"Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network","date":"2018-09-16","arxiv_id":"1809.05831","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-adversarial-perturbation-on-deep","title":"Robust Adversarial Perturbation on Deep Proposal-based Models","date":"2018-09-16","arxiv_id":"1809.05962","repositories_listed":0,"syntology":null},{"url":null,"slug":"keypoint-based-weakly-supervised-human","title":"Keypoint Based Weakly Supervised Human Parsing","date":"2018-09-14","arxiv_id":"1809.05285","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-semantic-segmentation-models-for","title":"Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective","date":"2018-09-13","arxiv_id":"1809.04730","repositories_listed":0,"syntology":null},{"url":null,"slug":"dispsegnet-leveraging-semantics-for-end-to","title":"DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery","date":"2018-09-13","arxiv_id":"1809.04734","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-group-and-label-fine-grained","title":"Learning to Group and Label Fine-Grained Shape Components","date":"2018-09-13","arxiv_id":"1809.05050","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-detection-and-segmentation-architecture-for","title":"A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images","date":"2018-09-11","arxiv_id":"1809.03917","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automatic-method-for-complete-brain-matter","title":"An Automatic Method for Complete Brain Matter Segmentation from Multislice CT scan","date":"2018-09-11","arxiv_id":"1809.06215","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-the","title":"Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging","date":"2018-09-11","arxiv_id":"1809.03788","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-segmentation-from-limited-training","title":"Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease","date":"2018-09-11","arxiv_id":"1809.04182","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiasing-semantic-segmentation-for-robot","title":"Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer","date":"2018-09-11","arxiv_id":"1809.03676","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-binary-image-segmentation-with","title":"Interactive Binary Image Segmentation with Edge Preservation","date":"2018-09-10","arxiv_id":"1809.03334","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-vs-deep-learning-architectures-for","title":"Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis","date":"2018-09-10","arxiv_id":"1809.03185","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-relationship-prediction-via-label","title":"Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information","date":"2018-09-09","arxiv_id":"1809.02945","repositories_listed":0,"syntology":null},{"url":null,"slug":"82-treebanks-34-models-universal-dependency","title":"82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models","date":"2018-09-06","arxiv_id":"1809.02237","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-importance-of-visual-context-for-data","title":"On the Importance of Visual Context for Data Augmentation in Scene Understanding","date":"2018-09-06","arxiv_id":"1809.02492","repositories_listed":0,"syntology":null},{"url":"/paper/panoptic-segmentation-with-a-joint-semantic","slug":"panoptic-segmentation-with-a-joint-semantic","title":"Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network","date":"2018-09-06","arxiv_id":"1809.02110","repositories_listed":0,"syntology":null},{"url":null,"slug":"youtube-vos-a-large-scale-video-object","title":"YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark","date":"2018-09-06","arxiv_id":"1809.03327","repositories_listed":0,"syntology":null},{"url":null,"slug":"retinal-vessel-segmentation-under-extreme-low","title":"Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach","date":"2018-09-05","arxiv_id":"1809.01348","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-smoke-segmentation","title":"Deep Smoke Segmentation","date":"2018-09-04","arxiv_id":"1809.00774","repositories_listed":0,"syntology":null},{"url":null,"slug":"penalizing-top-performers-conservative-loss","title":"Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation","date":"2018-09-04","arxiv_id":"1809.00903","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-iris-segmentation-based-on-fully","title":"Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks","date":"2018-09-04","arxiv_id":"1809.00769","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-video-object-segmentation-using","slug":"unsupervised-video-object-segmentation-using","title":"Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation","date":"2018-09-04","arxiv_id":"1809.01125","repositories_listed":0,"syntology":null},{"url":"/paper/videomatch-matching-based-video-object","slug":"videomatch-matching-based-video-object","title":"VideoMatch: Matching based Video Object Segmentation","date":"2018-09-04","arxiv_id":"1809.01123","repositories_listed":0,"syntology":null},{"url":null,"slug":"iou-is-not-submodular","title":"IoU is not submodular","date":"2018-09-03","arxiv_id":"1809.00593","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-recurrent-neural-networks-with-context","title":"3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/associating-inter-image-salient-instances-for","slug":"associating-inter-image-salient-instances-for","title":"Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-transfer-through-deep-activation","title":"Domain transfer through deep activation matching","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-joint-semantic-segmentation-of","title":"End-to-End Joint Semantic Segmentation of Actors and Actions in Video","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"focus-segment-and-erase-an-efficient-network","title":"Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-boundary-prediction-for-object","title":"Interactive Boundary Prediction for Object Selection","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/joint-task-recursive-learning-for-semantic","slug":"joint-task-recursive-learning-for-semantic","title":"Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"k-convexity-shape-priors-for-segmentation","title":"K-convexity shape priors for segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-context-intertwining-for-semantic","title":"Multi-Scale Context Intertwining for Semantic Segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rendering-portraitures-from-monocular-camera","title":"Rendering Portraitures from Monocular Camera and Beyond","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-clique-optimization-for-video","title":"Sequential Clique Optimization for Video Object Segmentation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"c4d20503b7871f97b1a2efde6b5c2c68e835a993f794af03ff27fb675c7c9fc4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}