{"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/103","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":103,"pages_in_order":148,"rows_per_page":100,"rows":[10201,10300],"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/102","next":"/task/semantic-segmentation/papers/104","papers":[{"url":"/paper/from-colouring-in-to-pointillism-revisiting","slug":"from-colouring-in-to-pointillism-revisiting","title":"From colouring-in to pointillism: revisiting semantic segmentation supervision","date":"2022-10-25","arxiv_id":"2210.14142","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-explicit-object-centric","title":"Learning Explicit Object-Centric Representations with Vision Transformers","date":"2022-10-25","arxiv_id":"2210.14139","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-batch-and-patch-size-training-for","title":"Large Batch and Patch Size Training for Medical Image Segmentation","date":"2022-10-24","arxiv_id":"2210.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-image-segmentation-with-deep-1","title":"Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping","date":"2022-10-24","arxiv_id":"2210.13296","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-efficient-iris-recognition-system","title":"Towards an efficient Iris Recognition System on Embedded Devices","date":"2022-10-24","arxiv_id":"2210.13101","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-deep-semantic-segmentation","title":"An Interpretable Deep Semantic Segmentation Method for Earth Observation","date":"2022-10-23","arxiv_id":"2210.12820","repositories_listed":0,"syntology":null},{"url":"/paper/towards-comprehensive-representation","slug":"towards-comprehensive-representation","title":"Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation","date":"2022-10-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-image-completion","title":"Instance-Aware Image Completion","date":"2022-10-22","arxiv_id":"2210.12350","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversity-promoting-ensemble-for-medical","title":"Diversity-Promoting Ensemble for Medical Image Segmentation","date":"2022-10-22","arxiv_id":"2210.12388","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-dc-unext-an-mlp-based-multi-scale-feature","title":"MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images","date":"2022-10-22","arxiv_id":"2210.12361","repositories_listed":0,"syntology":null},{"url":null,"slug":"slam-semantic-learning-based-activation-map","title":"SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation","date":"2022-10-22","arxiv_id":"2210.12417","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-fidelity-visual-structural-inspections","title":"High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers","date":"2022-10-21","arxiv_id":"2210.12175","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-image-semantic-segmentation","slug":"unsupervised-image-semantic-segmentation","title":"Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks","date":"2022-10-21","arxiv_id":"2210.11810","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-multi-object-segmentation-by","title":"Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns","date":"2022-10-21","arxiv_id":"2210.12148","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgtunet-an-new-unet-for-colon-nuclei-instance","title":"MGTUNet: An new UNet for colon nuclei instance segmentation and quantification","date":"2022-10-20","arxiv_id":"2210.10981","repositories_listed":0,"syntology":null},{"url":null,"slug":"rais-robust-and-accurate-interactive","title":"RAIS: Robust and Accurate Interactive Segmentation via Continual Learning","date":"2022-10-20","arxiv_id":"2210.10984","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferring-learned-patterns-from-ground","title":"Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming","date":"2022-10-20","arxiv_id":"2210.11545","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-deep-learning-1","title":"Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation","date":"2022-10-19","arxiv_id":"2210.10641","repositories_listed":0,"syntology":null},{"url":null,"slug":"havana-hard-negative-samples-aware-self","title":"HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation","date":"2022-10-19","arxiv_id":"2210.10626","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-semantic-relation-generation","title":"Image Semantic Relation Generation","date":"2022-10-19","arxiv_id":"2210.11253","repositories_listed":0,"syntology":null},{"url":null,"slug":"openearthmap-a-benchmark-dataset-for-global","title":"OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping","date":"2022-10-19","arxiv_id":"2210.10732","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-free-direct-iris-localization","title":"Segmentation-free Direct Iris Localization Networks","date":"2022-10-19","arxiv_id":"2210.10403","repositories_listed":0,"syntology":null},{"url":"/paper/number-adaptive-prototype-learning-for-3d","slug":"number-adaptive-prototype-learning-for-3d","title":"Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation","date":"2022-10-18","arxiv_id":"2210.09948","repositories_listed":0,"syntology":null},{"url":null,"slug":"otsu-based-differential-evolution-method-for","title":"Otsu based Differential Evolution Method for Image Segmentation","date":"2022-10-18","arxiv_id":"2210.10005","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-multi-modal-semantic-fusion-on-1","title":"Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation","date":"2022-10-18","arxiv_id":"2210.09739","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-point-cloud-segmentation-by","title":"Zero-shot point cloud segmentation by transferring geometric primitives","date":"2022-10-18","arxiv_id":"2210.09923","repositories_listed":0,"syntology":null},{"url":null,"slug":"cutting-splicing-data-augmentation-a-novel","title":"Cutting-Splicing data augmentation: A novel technology for medical image segmentation","date":"2022-10-17","arxiv_id":"2210.09099","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformably-scaled-transposed-convolution","title":"Deformably-Scaled Transposed Convolution","date":"2022-10-17","arxiv_id":"2210.09446","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-feature-distillation-network","title":"Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation","date":"2022-10-17","arxiv_id":"2210.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"pcr-pessimistic-consistency-regularization","title":"Fuzzy Positive Learning for Semi-supervised Semantic Segmentation","date":"2022-10-16","arxiv_id":"2210.08519","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-with-active-semi-1","title":"Semantic Segmentation with Active Semi-Supervised Representation Learning","date":"2022-10-16","arxiv_id":"2210.08403","repositories_listed":0,"syntology":null},{"url":"/paper/cordeep-and-the-sacrobosco-dataset-detection","slug":"cordeep-and-the-sacrobosco-dataset-detection","title":"CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents","date":"2022-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mkis-net-a-light-weight-multi-kernel-network","title":"MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation","date":"2022-10-15","arxiv_id":"2210.08168","repositories_listed":0,"syntology":null},{"url":"/paper/prediction-calibration-for-generalized-few","slug":"prediction-calibration-for-generalized-few","title":"Prediction Calibration for Generalized Few-shot Semantic Segmentation","date":"2022-10-15","arxiv_id":"2210.08290","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-limited-tissue-segmentation-using","title":"Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning","date":"2022-10-14","arxiv_id":"2210.07936","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-segmentation-with-cross-modal","title":"Instance Segmentation with Cross-Modal Consistency","date":"2022-10-14","arxiv_id":"2210.08113","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-label-efficient-semantic-segmentation","title":"LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds","date":"2022-10-14","arxiv_id":"2210.08064","repositories_listed":0,"syntology":null},{"url":null,"slug":"monodvps-a-self-supervised-monocular-depth","title":"MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation","date":"2022-10-14","arxiv_id":"2210.07577","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-based-video-anomaly","title":"Multi-Task Learning based Video Anomaly Detection with Attention","date":"2022-10-14","arxiv_id":"2210.07697","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-to-real-composite-semantic","title":"Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing","date":"2022-10-14","arxiv_id":"2210.07466","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-dense-nuclei-detection-and","title":"Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images","date":"2022-10-14","arxiv_id":"2210.07862","repositories_listed":0,"syntology":null},{"url":null,"slug":"alife-adaptive-logit-regularizer-and-feature","title":"ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation","date":"2022-10-13","arxiv_id":"2210.06816","repositories_listed":0,"syntology":null},{"url":"/paper/composite-learning-for-robust-and-effective","slug":"composite-learning-for-robust-and-effective","title":"Composite Learning for Robust and Effective Dense Predictions","date":"2022-10-13","arxiv_id":"2210.07239","repositories_listed":0,"syntology":null},{"url":"/paper/dcanet-differential-convolution-attention","slug":"dcanet-differential-convolution-attention","title":"DCANet: Differential Convolution Attention Network for RGB-D Semantic Segmentation","date":"2022-10-13","arxiv_id":"2210.06747","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-style-continual-semantic","title":"Learning with Style: Continual Semantic Segmentation Across Tasks and Domains","date":"2022-10-13","arxiv_id":"2210.07016","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-calibration-of-underrepresented","title":"On the calibration of underrepresented classes in LiDAR-based semantic segmentation","date":"2022-10-13","arxiv_id":"2210.06811","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-clustering-network-for-unsupervised","title":"ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation","date":"2022-10-12","arxiv_id":"2210.05944","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-instance-mixing-across-domains","title":"Hierarchical Instance Mixing across Domains in Aerial Segmentation","date":"2022-10-12","arxiv_id":"2210.06216","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-scene-completion-with-joint-color","title":"Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image","date":"2022-10-12","arxiv_id":"2210.05891","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-video-pretraining-yields","title":"Self-supervised video pretraining yields robust and more human-aligned visual representations","date":"2022-10-12","arxiv_id":"2210.06433","repositories_listed":0,"syntology":null},{"url":null,"slug":"digitization-of-raster-logs-a-deep-learning","title":"Digitization of Raster Logs: A Deep Learning Approach","date":"2022-10-11","arxiv_id":"2210.05597","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpanet-dual-pooling-attention-network-for","title":"DPANET:Dual Pooling Attention Network for Semantic Segmentation","date":"2022-10-11","arxiv_id":"2210.05437","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypergraph-convolutional-networks-for-weakly","title":"Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation","date":"2022-10-11","arxiv_id":"2210.05564","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-transformers-for-full-context-and-high","title":"Memory transformers for full context and high-resolution 3D Medical Segmentation","date":"2022-10-11","arxiv_id":"2210.05313","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fast-and-accurate-approach-to-detection","title":"The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning","date":"2022-10-11","arxiv_id":"2210.05167","repositories_listed":0,"syntology":null},{"url":null,"slug":"ugformer-for-robust-left-atrium-and-scar","title":"UGformer for Robust Left Atrium and Scar Segmentation Across Scanners","date":"2022-10-11","arxiv_id":"2210.05151","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-an-evolving-class-ontology","title":"Continual Learning with Evolving Class Ontologies","date":"2022-10-10","arxiv_id":"2210.04993","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-equivariant-u-net","title":"Scale Equivariant U-Net","date":"2022-10-10","arxiv_id":"2210.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptive-fundus-image-1","title":"Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data","date":"2022-10-10","arxiv_id":"2210.04379","repositories_listed":0,"syntology":null},{"url":"/paper/transformer-based-flood-scene-segmentation","slug":"transformer-based-flood-scene-segmentation","title":"Transformer-based Flood Scene Segmentation for Developing Countries","date":"2022-10-09","arxiv_id":"2210.04218","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-fine-grain-segmentation-via","title":"Improving Data-Efficient Fossil Segmentation via Model Editing","date":"2022-10-08","arxiv_id":"2210.03879","repositories_listed":0,"syntology":null},{"url":"/paper/sequential-ensembling-for-semantic","slug":"sequential-ensembling-for-semantic","title":"Sequential Ensembling for Semantic Segmentation","date":"2022-10-08","arxiv_id":"2210.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"idpl-intra-subdomain-adaptation-adversarial","title":"IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels","date":"2022-10-07","arxiv_id":"2210.03435","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-self-supervised-representation","title":"Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision","date":"2022-10-07","arxiv_id":"2210.03289","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-space-transformers-for-video-panoptic","title":"Time-Space Transformers for Video Panoptic Segmentation","date":"2022-10-07","arxiv_id":"2210.03546","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-preserving-segmentation-network","title":"A Learning-based Framework for Topology-Preserving Segmentation using Quasiconformal Mappings","date":"2022-10-07","arxiv_id":"2210.03299","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modality-domain-adaptation-for-1","title":"Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline","date":"2022-10-06","arxiv_id":"2210.02991","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-unknown-image","title":"Domain Adaptation for Unknown Image Distortions in Instance Segmentation","date":"2022-10-05","arxiv_id":"2210.02386","repositories_listed":0,"syntology":null},{"url":null,"slug":"priornet-lesion-segmentation-in-pet-ct","title":"PriorNet: lesion segmentation in PET-CT including prior tumor appearance information","date":"2022-10-05","arxiv_id":"2210.02203","repositories_listed":0,"syntology":null},{"url":null,"slug":"adawac-adaptively-weighted-augmentation","title":"Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift","date":"2022-10-04","arxiv_id":"2210.01891","repositories_listed":0,"syntology":null},{"url":null,"slug":"asap-accurate-semantic-segmentation-for-real","title":"ASAP: Accurate semantic segmentation for real time performance","date":"2022-10-04","arxiv_id":"2210.01323","repositories_listed":0,"syntology":null},{"url":"/paper/k-means-for-unsupervised-instance","slug":"k-means-for-unsupervised-instance","title":"K-means for unsupervised instance segmentation using a self-supervised transformer","date":"2022-10-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-pre-training-for-semantic","title":"Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene","date":"2022-10-04","arxiv_id":"2210.01884","repositories_listed":0,"syntology":null},{"url":"/paper/toward-edge-efficient-dense-predictions-with","slug":"toward-edge-efficient-dense-predictions-with","title":"Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search","date":"2022-10-04","arxiv_id":"2210.01384","repositories_listed":0,"syntology":null},{"url":null,"slug":"nas-based-recursive-stage-partial-network","title":"NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation","date":"2022-10-03","arxiv_id":"2210.00698","repositories_listed":0,"syntology":null},{"url":null,"slug":"wild-animal-classifier-using-cnn","title":"Wild Animal Classifier Using CNN","date":"2022-10-03","arxiv_id":"2210.07973","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaia-graphical-information-gain-based","title":"GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation","date":"2022-10-02","arxiv_id":"2210.01558","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-multi-view-object-segmentation","title":"Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation","date":"2022-10-02","arxiv_id":"2210.00489","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-augmented-convnext-unet-for-rectal","title":"Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation","date":"2022-10-01","arxiv_id":"2210.00227","repositories_listed":0,"syntology":null},{"url":null,"slug":"viewpoint-planning-based-on-shape-completion","title":"NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction","date":"2022-09-30","arxiv_id":"2209.15376","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-should-i-spend-my-flops-efficiency","title":"Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods","date":"2022-09-30","arxiv_id":"2209.15589","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-satellite-building-construction","title":"Automatic satellite building construction monitoring","date":"2022-09-29","arxiv_id":"2209.15084","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-physical-adversarial-attack-in","title":"A Survey on Physical Adversarial Attack in Computer Vision","date":"2022-09-28","arxiv_id":"2209.14262","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-mobile-attention-kpconv-networks-1","title":"Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor","date":"2022-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-using-feature-generation","title":"Data Augmentation using Feature Generation for Volumetric Medical Images","date":"2022-09-28","arxiv_id":"2209.14097","repositories_listed":0,"syntology":null},{"url":null,"slug":"road-rutting-detection-using-deep-learning-on","title":"Road Rutting Detection using Deep Learning on Images","date":"2022-09-28","arxiv_id":"2209.14225","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-vegetation-in-remote","title":"Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning","date":"2022-09-28","arxiv_id":"2209.14364","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-attention-mechanism","title":"A comparative study of attention mechanism and generative adversarial network in facade damage segmentation","date":"2022-09-27","arxiv_id":"2209.13283","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamics-aware-spatiotemporal-occupancy","title":"Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments","date":"2022-09-27","arxiv_id":"2209.13172","repositories_listed":0,"syntology":null},{"url":"/paper/freeseg-free-mask-from-interpretable","slug":"freeseg-free-mask-from-interpretable","title":"FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation","date":"2022-09-27","arxiv_id":"2209.13558","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-attacks-on-artificial-intelligence","title":"Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals","date":"2022-09-27","arxiv_id":"2209.13007","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-domain-training-improves-multi-mission","title":"Mixed-domain Training Improves Multi-Mission Terrain Segmentation","date":"2022-09-27","arxiv_id":"2209.13674","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-and-equivariant-spherical-cnns-by","title":"Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions","date":"2022-09-27","arxiv_id":"2209.13603","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multimodal-multitask-scene","title":"Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents","date":"2022-09-27","arxiv_id":"2209.13156","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversified-dynamic-routing-for-vision-tasks","title":"Diversified Dynamic Routing for Vision Tasks","date":"2022-09-26","arxiv_id":"2209.13071","repositories_listed":0,"syntology":null},{"url":null,"slug":"erase-net-efficient-segmentation-networks-for","title":"ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals","date":"2022-09-26","arxiv_id":"2209.12940","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-uniform-representation-learning-method-for","title":"A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction","date":"2022-09-25","arxiv_id":"2209.12208","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-to-image-translation-for-autonomous","title":"Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs","date":"2022-09-23","arxiv_id":"2209.11673","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-test-time-self-training-under","title":"TeST: Test-time Self-Training under Distribution Shift","date":"2022-09-23","arxiv_id":"2209.11459","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-perception-models-for-off","title":"Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles","date":"2022-09-22","arxiv_id":"2209.11115","repositories_listed":0,"syntology":null}],"record_sha256":"5563539571814fc440949eba986facb5a3f14622b158407ce4ad7eb290e9a024","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}