{"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/109","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":109,"pages_in_order":148,"rows_per_page":100,"rows":[10801,10900],"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/108","next":"/task/semantic-segmentation/papers/110","papers":[{"url":null,"slug":"a-novel-shape-based-loss-function-for-machine","title":"A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging","date":"2022-03-07","arxiv_id":"2203.03336","repositories_listed":0,"syntology":null},{"url":null,"slug":"cartoon-texture-evolution-for-two-region","title":"Cartoon-texture evolution for two-region image segmentation","date":"2022-03-07","arxiv_id":"2203.03513","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-and-classification-of-low","title":"Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver","date":"2022-03-07","arxiv_id":"2203.03636","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-sims-semi-parametric-image-and-depth","title":"Depth-SIMS: Semi-Parametric Image and Depth Synthesis","date":"2022-03-07","arxiv_id":"2203.03405","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-robot-navigation-with-self","title":"Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers","date":"2022-03-07","arxiv_id":"2203.03682","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-steering-multi-annotations-per-sample-for","title":"On Steering Multi-Annotations per Sample for Multi-Task Learning","date":"2022-03-06","arxiv_id":"2203.02946","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-proposal-rectification-towards-robust","title":"Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images","date":"2022-03-06","arxiv_id":"2203.02846","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-aspects-a-segmentation-assisted-model","title":"Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement","date":"2022-03-05","arxiv_id":"2203.03622","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-dirichlet-process-gaussian","title":"Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images","date":"2022-03-05","arxiv_id":"2203.02820","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-coastline-extraction-in-sar","title":"High-resolution Coastline Extraction in SAR Images via MISP-GGD Superpixel Segmentation","date":"2022-03-05","arxiv_id":"2203.02708","repositories_listed":0,"syntology":null},{"url":null,"slug":"idmunet-a-new-image-decomposition-induced","title":"$\\ell_1$DecNet+: A new architecture framework by $\\ell_1$ decomposition and iteration unfolding for sparse feature segmentation","date":"2022-03-05","arxiv_id":"2203.02690","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-part-aware-instance","title":"Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking","date":"2022-03-05","arxiv_id":"2203.02767","repositories_listed":0,"syntology":null},{"url":null,"slug":"carbon-footprint-of-selecting-and-training","title":"Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis","date":"2022-03-04","arxiv_id":"2203.02202","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixcl-pixel-label-matters-to-contrastive","title":"MixCL: Pixel label matters to contrastive learning","date":"2022-03-04","arxiv_id":"2203.02114","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-of-reusable-abstract-models","title":"Online Learning of Reusable Abstract Models for Object Goal Navigation","date":"2022-03-04","arxiv_id":"2203.02583","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-segmentation-of-33-anatomies","title":"Universal Segmentation of 33 Anatomies","date":"2022-03-04","arxiv_id":"2203.02098","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-path-planning-for-uavs-for-multi","title":"Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation","date":"2022-03-03","arxiv_id":"2203.01642","repositories_listed":0,"syntology":null},{"url":null,"slug":"color-space-based-hover-net-for-nuclei","title":"Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification","date":"2022-03-03","arxiv_id":"2203.01940","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-video-instance-segmentation-via","title":"Efficient Video Instance Segmentation via Tracklet Query and Proposal","date":"2022-03-03","arxiv_id":"2203.01853","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-neural-architecture-search-for","title":"Fast Neural Architecture Search for Lightweight Dense Prediction Networks","date":"2022-03-03","arxiv_id":"2203.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"panoptic-segmentation-with-highly-imbalanced","title":"Panoptic segmentation with highly imbalanced semantic labels","date":"2022-03-03","arxiv_id":"2203.11692","repositories_listed":0,"syntology":null},{"url":null,"slug":"segtad-precise-temporal-action-detection-via","title":"SegTAD: Precise Temporal Action Detection via Semantic Segmentation","date":"2022-03-03","arxiv_id":"2203.01542","repositories_listed":0,"syntology":null},{"url":"/paper/aggregated-pyramid-vision-transformer-split","slug":"aggregated-pyramid-vision-transformer-split","title":"Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions","date":"2022-03-02","arxiv_id":"2203.00960","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-lidar-based-semantic-segmentation","title":"Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations","date":"2022-03-02","arxiv_id":"2203.01151","repositories_listed":0,"syntology":null},{"url":null,"slug":"sea-bridging-the-gap-between-one-and-two","title":"SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment","date":"2022-03-02","arxiv_id":"2203.00862","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-constrained-cnn-for-segmentation-guided","title":"Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI","date":"2022-03-02","arxiv_id":"2203.01089","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-corrected-multi-scale-fusion-network","title":"Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation","date":"2022-03-01","arxiv_id":"2203.00436","repositories_listed":0,"syntology":null},{"url":null,"slug":"colon-nuclei-instance-segmentation-using-a","title":"Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector","date":"2022-03-01","arxiv_id":"2203.01321","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-certain-are-your-uncertainties","title":"Uncertainty categories in medical image segmentation: a study of source-related diversity","date":"2022-03-01","arxiv_id":"2203.00238","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-analysis-for-automatic-measurement-of","title":"Image analysis for automatic measurement of crustose lichens","date":"2022-03-01","arxiv_id":"2203.00787","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-challenges-when-3d-semantic","title":"Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data","date":"2022-03-01","arxiv_id":"2203.00214","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-semantic-segmentation-from-multiple","title":"Learning Semantic Segmentation from Multiple Datasets with Label Shifts","date":"2022-02-28","arxiv_id":"2202.14030","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-transformer-attention-network","title":"Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud","date":"2022-02-28","arxiv_id":"2203.00138","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-device-independent-deep-learning","title":"Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation","date":"2022-02-28","arxiv_id":"2202.13553","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-datasetgan-in-medical-imaging","title":"Application of DatasetGAN in medical imaging: preliminary studies","date":"2022-02-27","arxiv_id":"2202.13463","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-label-aware-superpixels-for-multi","title":"Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery","date":"2022-02-27","arxiv_id":"2202.13487","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-preserving-segmentation-network-a","title":"Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component","date":"2022-02-27","arxiv_id":"2202.13331","repositories_listed":0,"syntology":null},{"url":null,"slug":"dgss-domain-generalized-semantic-segmentation","title":"DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment","date":"2022-02-26","arxiv_id":"2202.13144","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-point-cloud-semantic","title":"Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning","date":"2022-02-25","arxiv_id":"2202.12588","repositories_listed":0,"syntology":null},{"url":null,"slug":"ciscnet-a-single-branch-cell-instance","title":"ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network","date":"2022-02-25","arxiv_id":"2202.13960","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-calibration-for-object-detection","title":"Confidence Calibration for Object Detection and Segmentation","date":"2022-02-25","arxiv_id":"2202.12785","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-instance-segmentation-using-2","title":"Weakly Supervised Instance Segmentation using Motion Information via Optical Flow","date":"2022-02-25","arxiv_id":"2202.13006","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-variation-aware-medical-image","title":"Data variation-aware medical image segmentation","date":"2022-02-24","arxiv_id":"2202.12099","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-video-segmentation-models-with-per","title":"Efficient Video Segmentation Models with Per-frame Inference","date":"2022-02-24","arxiv_id":"2202.12427","repositories_listed":0,"syntology":null},{"url":"/paper/fully-self-supervised-learning-for-semantic","slug":"fully-self-supervised-learning-for-semantic","title":"Fully Self-Supervised Learning for Semantic Segmentation","date":"2022-02-24","arxiv_id":"2202.11981","repositories_listed":0,"syntology":null},{"url":"/paper/amodal-panoptic-segmentation","slug":"amodal-panoptic-segmentation","title":"Amodal Panoptic Segmentation","date":"2022-02-23","arxiv_id":"2202.11542","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-block-neural-architecture-search-for","title":"Mixed-Block Neural Architecture Search for Medical Image Segmentation","date":"2022-02-23","arxiv_id":"2202.11401","repositories_listed":0,"syntology":null},{"url":"/paper/thermal-hand-image-segmentation-for-biometric","slug":"thermal-hand-image-segmentation-for-biometric","title":"Thermal hand image segmentation for biometric recognition","date":"2022-02-23","arxiv_id":"2202.11462","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-eye-driving-with-the-eyes-of-ai-for-corner","title":"A-Eye: Driving with the Eyes of AI for Corner Case Generation","date":"2022-02-22","arxiv_id":"2202.10803","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-object-aware-hybrid-u-net-for-breast","title":"An Object Aware Hybrid U-Net for Breast Tumour Annotation","date":"2022-02-22","arxiv_id":"2202.10691","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-looming-from-lidar","title":"Estimation of Looming from LiDAR","date":"2022-02-22","arxiv_id":"2202.10972","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-free-object-segments-for-long","title":"Learning with Free Object Segments for Long-Tailed Instance Segmentation","date":"2022-02-22","arxiv_id":"2202.11124","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointmatch-a-consistency-training-framework","title":"PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds","date":"2022-02-22","arxiv_id":"2202.10705","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-semantic-assisted-outlier-removal-for","title":"Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration","date":"2022-02-21","arxiv_id":"2202.10579","repositories_listed":0,"syntology":null},{"url":null,"slug":"pcscnet-fast-3d-semantic-segmentation-of","title":"PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Network","date":"2022-02-21","arxiv_id":"2202.10047","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-the-zigzag-flattening-for-image","title":"Rethinking the Zigzag Flattening for Image Reading","date":"2022-02-21","arxiv_id":"2202.10240","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-with-quantitative","title":"A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements","date":"2022-02-18","arxiv_id":"2202.09020","repositories_listed":0,"syntology":null},{"url":null,"slug":"af-2-adaptive-focus-framework-for-aerial-1","title":"AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation","date":"2022-02-18","arxiv_id":"2202.10322","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-and-contrastive-learning-framework","title":"An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation","date":"2022-02-18","arxiv_id":"2202.09002","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-learning-for-instance-segmentation","title":"Iterative Learning for Instance Segmentation","date":"2022-02-18","arxiv_id":"2202.09110","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-learning-of-frequency-and-spatial","title":"Joint Learning of Frequency and Spatial Domains for Dense Predictions","date":"2022-02-18","arxiv_id":"2202.08991","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-and-learning-the-unknown-in","title":"Detecting and Learning the Unknown in Semantic Segmentation","date":"2022-02-17","arxiv_id":"2202.08700","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-neuron-instance-segmentation-based","title":"A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing","date":"2022-02-17","arxiv_id":"2202.08682","repositories_listed":0,"syntology":null},{"url":null,"slug":"mirror-yolo-an-attention-based-instance","title":"Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model","date":"2022-02-17","arxiv_id":"2202.08498","repositories_listed":0,"syntology":null},{"url":null,"slug":"shift-memory-network-for-temporal-scene","title":"Shift-Memory Network for Temporal Scene Segmentation","date":"2022-02-17","arxiv_id":"2202.08399","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-dense","title":"Neural Architecture Search for Dense Prediction Tasks in Computer Vision","date":"2022-02-15","arxiv_id":"2202.07242","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-state-of-the-art-survey-of-u-net-in","title":"A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification","date":"2022-02-14","arxiv_id":"2202.06465","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-preserving-instance-level","title":"Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection","date":"2022-02-14","arxiv_id":"2202.06513","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-adaptive-feature-transformer-with","title":"Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation","date":"2022-02-14","arxiv_id":"2202.06498","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-medical-image-segmentation-3","title":"Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training","date":"2022-02-12","arxiv_id":"2202.06104","repositories_listed":0,"syntology":null},{"url":"/paper/peg-transfer-workflow-recognition-challenge","slug":"peg-transfer-workflow-recognition-challenge","title":"PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?","date":"2022-02-11","arxiv_id":"2202.05821","repositories_listed":0,"syntology":null},{"url":null,"slug":"amplitude-spectrum-transformation-for-open","title":"Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation","date":"2022-02-09","arxiv_id":"2202.04287","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-strategy-for-fine-tuning","title":"Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data","date":"2022-02-09","arxiv_id":"2202.04766","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-anaemic-rbcs-using","title":"Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network","date":"2022-02-09","arxiv_id":"2202.04650","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-image-descriptor-with-aggregated","title":"A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition","date":"2022-02-08","arxiv_id":"2202.03677","repositories_listed":0,"syntology":null},{"url":null,"slug":"scr-smooth-contour-regression-with-geometric","title":"SCR: Smooth Contour Regression with Geometric Priors","date":"2022-02-08","arxiv_id":"2202.03784","repositories_listed":0,"syntology":null},{"url":"/paper/stc-spatio-temporal-contrastive-learning-for","slug":"stc-spatio-temporal-contrastive-learning-for","title":"STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation","date":"2022-02-08","arxiv_id":"2202.03747","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-transmission-of-images-with-the","title":"Wireless Transmission of Images With The Assistance of Multi-level Semantic Information","date":"2022-02-08","arxiv_id":"2202.04754","repositories_listed":0,"syntology":null},{"url":null,"slug":"corrupted-image-modeling-for-self-supervised","title":"Corrupted Image Modeling for Self-Supervised Visual Pre-Training","date":"2022-02-07","arxiv_id":"2202.03382","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-ferns-for-semantic-segmentation-of","title":"Random Ferns for Semantic Segmentation of PolSAR Images","date":"2022-02-07","arxiv_id":"2202.03498","repositories_listed":0,"syntology":null},{"url":null,"slug":"soda-self-organizing-data-augmentation-in","title":"SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks","date":"2022-02-07","arxiv_id":"2202.03223","repositories_listed":0,"syntology":null},{"url":null,"slug":"sud-supervision-by-denoising-for-medical","title":"Supervision by Denoising for Medical Image Segmentation","date":"2022-02-07","arxiv_id":"2202.02952","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-sensor-fusion-for-auto-driving","title":"Multi-modal Sensor Fusion for Auto Driving Perception: A Survey","date":"2022-02-06","arxiv_id":"2202.02703","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-on-3d-point-clouds-by","title":"Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting","date":"2022-02-05","arxiv_id":"2202.02543","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-aware-information-maximization-for","title":"Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation","date":"2022-02-04","arxiv_id":"2202.02371","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepstaple-learning-to-predict-multimodal","title":"DeepSTAPLE: Learning to predict multimodal registration quality for unsupervised domain adaptation","date":"2022-02-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"heed-the-noise-in-performance-evaluations-in","title":"Heed the Noise in Performance Evaluations in Neural Architecture Search","date":"2022-02-04","arxiv_id":"2202.02078","repositories_listed":0,"syntology":null},{"url":null,"slug":"standardsim-a-synthetic-dataset-for-retail","title":"StandardSim: A Synthetic Dataset For Retail Environments","date":"2022-02-04","arxiv_id":"2202.02418","repositories_listed":0,"syntology":null},{"url":"/paper/the-devil-is-in-the-labels-semantic","slug":"the-devil-is-in-the-labels-semantic","title":"Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings","date":"2022-02-04","arxiv_id":"2202.02002","repositories_listed":0,"syntology":null},{"url":null,"slug":"docbed-a-multi-stage-ocr-solution-for","title":"DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts","date":"2022-02-03","arxiv_id":"2202.01414","repositories_listed":0,"syntology":null},{"url":null,"slug":"automotive-parts-assessment-applying-real","title":"Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts","date":"2022-02-02","arxiv_id":"2202.00884","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-sensor-binarized-fully-convolutional","title":"On-Sensor Binarized Fully Convolutional Neural Network with A Pixel Processor Array","date":"2022-02-02","arxiv_id":"2202.00836","repositories_listed":0,"syntology":null},{"url":null,"slug":"access-control-of-object-detection-models","title":"Access Control of Object Detection Models Using Encrypted Feature Maps","date":"2022-02-01","arxiv_id":"2202.00265","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-explanations-to-segmentation-using","title":"From Explanations to Segmentation: Using Explainable AI for Image Segmentation","date":"2022-02-01","arxiv_id":"2202.00315","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvp-net-multiple-view-pointwise-semantic","title":"MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds","date":"2022-01-30","arxiv_id":"2201.12769","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-semi-supervised-neural-architecture","title":"Self Semi Supervised Neural Architecture Search for Semantic Segmentation","date":"2022-01-29","arxiv_id":"2201.12646","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-visualization-and-spatial-data-mining-for","title":"3D Visualization and Spatial Data Mining for Analysis of LULC Images","date":"2022-01-28","arxiv_id":"2202.00123","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-aware-generative-adversarial","title":"Class-Aware Adversarial Transformers for Medical Image Segmentation","date":"2022-01-26","arxiv_id":"2201.10737","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-liver-and-hepatic-lesion-segmentation","title":"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers","date":"2022-01-26","arxiv_id":"2201.10981","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-low-can-we-go-pixel-annotation-for","title":"How Low Can We Go? Pixel Annotation for Semantic Segmentation","date":"2022-01-25","arxiv_id":"2201.10448","repositories_listed":0,"syntology":null}],"record_sha256":"40362dc81bbba524bbb118c7b7bd2744e376637ab54444f8e029ccf730bf06f2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}