{"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":"/method/spatial-pyramid-pooling/papers/2","list_of":"/method/spatial-pyramid-pooling","method":"Spatial Pyramid Pooling","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":285,"counts":{"archive_papers_tagged":285,"with_a_code_link":119,"where_syntology_ran_a_sample":23,"not_listed_spam_title":0,"listed":285,"listed_where_code_ran":23,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":20,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":20,"listed_every_run_a_failure_of_syntologys_instrument":3,"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":"/method/spatial-pyramid-pooling","prev":"/method/spatial-pyramid-pooling","next":"/method/spatial-pyramid-pooling/papers/3","papers":[{"paper":null,"slug":"roma-run-time-object-detection-to-maximize","title":"ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy","date":"2022-10-28","arxiv_id":"2210.16083","n_code_links":0,"syntology":null},{"paper":"/paper/a-knowledge-driven-vowel-based-approach-of","slug":"a-knowledge-driven-vowel-based-approach-of","title":"A knowledge-driven vowel-based approach of depression classification from speech using data augmentation","date":"2022-10-27","arxiv_id":"2210.15261","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-attentional-untargeted-adversarial","title":"Object-Attentional Untargeted Adversarial Attack","date":"2022-10-16","arxiv_id":"2210.08472","n_code_links":0,"syntology":null},{"paper":"/paper/calving-fronts-and-where-to-find-them-a","slug":"calving-fronts-and-where-to-find-them-a","title":"Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery","date":"2022-09-22","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/self-adversarial-multi-scale-contrastive","slug":"self-adversarial-multi-scale-contrastive","title":"Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images","date":"2022-09-21","arxiv_id":"2209.10700","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["physiologicailab/sam-cl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/macab-model-agnostic-clean-annotation","slug":"macab-model-agnostic-clean-annotation","title":"TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World","date":"2022-09-06","arxiv_id":"2209.02339","n_code_links":1,"syntology":null},{"paper":"/paper/histoseg-quick-attention-with-multi-loss","slug":"histoseg-quick-attention-with-multi-loss","title":"HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images","date":"2022-09-01","arxiv_id":"2209.00729","n_code_links":1,"syntology":null},{"paper":null,"slug":"swin-transformer-yolov5-for-real-time-wine","title":"Swin-transformer-yolov5 For Real-time Wine Grape Bunch Detection","date":"2022-08-30","arxiv_id":"2208.14508","n_code_links":0,"syntology":null},{"paper":"/paper/ammunition-component-classification-using","slug":"ammunition-component-classification-using","title":"Ammunition Component Classification Using Deep Learning","date":"2022-08-26","arxiv_id":"2208.12863","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparison-of-object-detection-algorithms-for","title":"Comparison of Object Detection Algorithms for Street-level Objects","date":"2022-08-24","arxiv_id":"2208.11315","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-mask-wearing-detection-of-natural","title":"A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4","date":"2022-08-24","arxiv_id":"2208.11353","n_code_links":0,"syntology":null},{"paper":"/paper/threshold-adaptive-unsupervised-focal-loss","slug":"threshold-adaptive-unsupervised-focal-loss","title":"Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation","date":"2022-08-23","arxiv_id":"2208.10716","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-accident-detection-in-traffic","slug":"real-time-accident-detection-in-traffic","title":"Real-Time Accident Detection in Traffic Surveillance Using Deep Learning","date":"2022-08-12","arxiv_id":"2208.06461","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-detection-using-sim2real-domain","title":"Object Detection Using Sim2Real Domain Randomization for Robotic Applications","date":"2022-08-08","arxiv_id":"2208.04171","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiclass-asma-vs-targeted-pgd-attack-in","title":"Multiclass ASMA vs Targeted PGD Attack in Image Segmentation","date":"2022-08-03","arxiv_id":"2208.01844","n_code_links":0,"syntology":null},{"paper":"/paper/transdeeplab-convolution-free-transformer","slug":"transdeeplab-convolution-free-transformer","title":"TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation","date":"2022-08-01","arxiv_id":"2208.00713","n_code_links":1,"syntology":null},{"paper":"/paper/training-a-universal-instance-segmentation","slug":"training-a-universal-instance-segmentation","title":"Training a universal instance segmentation network for live cell images of various cell types and imaging modalities","date":"2022-07-28","arxiv_id":"2207.14347","n_code_links":1,"syntology":null},{"paper":null,"slug":"traffic-sign-detection-with-event-cameras-and","title":"Traffic Sign Detection With Event Cameras and DCNN","date":"2022-07-27","arxiv_id":"2207.13345","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-alignment-and-spatial-roi-module","title":"Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis","date":"2022-07-04","arxiv_id":"2207.01345","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-detection-of-rice-disease-in-images","title":"Automatic Detection of Rice Disease in Images of Various Leaf Sizes","date":"2022-06-15","arxiv_id":"2206.07344","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-object-detector-ensembles-for","title":"Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams","date":"2022-06-15","arxiv_id":"2206.07580","n_code_links":0,"syntology":null},{"paper":"/paper/making-sense-of-dependence-efficient-black","slug":"making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","arxiv_id":"2206.06219","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["paulnovello/hsic-attribution-method"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"research-on-smoking-behavior-detection-system","title":"Research on Smoking Behavior Detection System Based on Deep Learning","date":"2022-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-in-indian-food-platters","title":"Object Detection in Indian Food Platters using Transfer Learning with YOLOv4","date":"2022-05-10","arxiv_id":"2205.04841","n_code_links":0,"syntology":null},{"paper":"/paper/masked-generative-distillation","slug":"masked-generative-distillation","title":"Masked Generative Distillation","date":"2022-05-03","arxiv_id":"2205.01529","n_code_links":3,"syntology":null},{"paper":null,"slug":"birds-eye-view-measuring-behavior-and-posture","title":"Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being","date":"2022-04-29","arxiv_id":"2205.00069","n_code_links":0,"syntology":null},{"paper":"/paper/hrplanes-high-resolution-airplane-dataset-for","slug":"hrplanes-high-resolution-airplane-dataset-for","title":"A benchmark dataset for deep learning-based airplane detection: HRPlanes","date":"2022-04-22","arxiv_id":"2204.10959","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-automatic-detection-of-2","title":"Deep Learning based Automatic Detection of Dicentric Chromosome","date":"2022-04-17","arxiv_id":"2204.08029","n_code_links":0,"syntology":null},{"paper":"/paper/pp-yoloe-an-evolved-version-of-yolo","slug":"pp-yoloe-an-evolved-version-of-yolo","title":"PP-YOLOE: An evolved version of YOLO","date":"2022-03-30","arxiv_id":"2203.16250","n_code_links":8,"syntology":{"ran":22,"of":27,"n_ran_checked":21,"n_instrument":1,"unverified":5,"pointer_only":1,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 2 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["PaddlePaddle/PaddleDetection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/towards-robust-semantic-segmentation-of","slug":"towards-robust-semantic-segmentation-of","title":"Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning","date":"2022-03-19","arxiv_id":"2203.10395","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysis-and-adaptation-of-yolov4-for-object","title":"Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images","date":"2022-03-18","arxiv_id":"2203.10194","n_code_links":0,"syntology":null},{"paper":"/paper/autonomous-mosquito-habitat-detection-using","slug":"autonomous-mosquito-habitat-detection-using","title":"Autonomous Mosquito Habitat Detection Using Satellite Imagery and Convolutional Neural Networks for Disease Risk Mapping","date":"2022-03-09","arxiv_id":"2203.04463","n_code_links":1,"syntology":null},{"paper":"/paper/signature-and-log-signature-for-the-study-of","slug":"signature-and-log-signature-for-the-study-of","title":"Signature and Log-signature for the Study of Empirical Distributions Generated with GANs","date":"2022-03-07","arxiv_id":"2203.03226","n_code_links":1,"syntology":null},{"paper":"/paper/muad-multiple-uncertainties-for-autonomous","slug":"muad-multiple-uncertainties-for-autonomous","title":"MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks","date":"2022-03-02","arxiv_id":"2203.01437","n_code_links":3,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":"/paper/vehicle-and-license-plate-recognition-with","slug":"vehicle-and-license-plate-recognition-with","title":"Vehicle and License Plate Recognition with Novel Dataset for Toll Collection","date":"2022-02-11","arxiv_id":"2202.05631","n_code_links":3,"syntology":null},{"paper":"/paper/a-novel-encoder-decoder-network-with-guided","slug":"a-novel-encoder-decoder-network-with-guided","title":"A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing","date":"2022-02-08","arxiv_id":"2202.04757","n_code_links":1,"syntology":null},{"paper":null,"slug":"integrated-multiscale-domain-adaptive-yolo","title":"Integrated Multiscale Domain Adaptive YOLO","date":"2022-02-07","arxiv_id":"2202.03527","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-paced-learning-to-improve-text-row","title":"Self-paced learning to improve text row detection in historical documents with missing labels","date":"2022-01-28","arxiv_id":"2201.12216","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-defense-of-kalman-filtering-for-polyp","title":"In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos","date":"2022-01-27","arxiv_id":"2201.11450","n_code_links":0,"syntology":null},{"paper":"/paper/image-based-automatic-dial-meter-reading-in","slug":"image-based-automatic-dial-meter-reading-in","title":"Image-based Automatic Dial Meter Reading in Unconstrained Scenarios","date":"2022-01-08","arxiv_id":"2201.02850","n_code_links":1,"syntology":null},{"paper":"/paper/gpu-net-lightweight-u-net-with-more-diverse","slug":"gpu-net-lightweight-u-net-with-more-diverse","title":"GPU-Net: Lightweight U-Net with more diverse features","date":"2022-01-07","arxiv_id":"2201.02656","n_code_links":1,"syntology":null},{"paper":"/paper/lawin-transformer-improving-semantic","slug":"lawin-transformer-improving-semantic","title":"Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention","date":"2022-01-05","arxiv_id":"2201.01615","n_code_links":3,"syntology":null},{"paper":null,"slug":"multi-scale-feature-fusion-learning-better","title":"Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection","date":"2021-12-24","arxiv_id":"2112.13082","n_code_links":0,"syntology":null},{"paper":null,"slug":"probabilistic-approach-for-road-users","title":"Probabilistic Approach for Road-Users Detection","date":"2021-12-02","arxiv_id":"2112.01360","n_code_links":0,"syntology":null},{"paper":null,"slug":"sci-net-a-scale-invariant-model-for-building","title":"Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery","date":"2021-11-12","arxiv_id":"2111.06812","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-fast-accurate-fine-grain-object-detection","title":"A fast accurate fine-grain object detection model based on YOLOv4 deep neural network","date":"2021-10-30","arxiv_id":"2111.00298","n_code_links":0,"syntology":null},{"paper":null,"slug":"new-sar-target-recognition-based-on-yolo-and","title":"New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis","date":"2021-10-28","arxiv_id":"2110.15383","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-segmentation-for-urban-scene-images","slug":"semantic-segmentation-for-urban-scene-images","title":"Semantic Segmentation for Urban-Scene Images","date":"2021-10-20","arxiv_id":"2110.13813","n_code_links":1,"syntology":null},{"paper":"/paper/loveda-a-remote-sensing-land-cover-dataset","slug":"loveda-a-remote-sensing-land-cover-dataset","title":"LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation","date":"2021-10-17","arxiv_id":"2110.08733","n_code_links":5,"syntology":{"ran":4,"of":6,"n_ran_checked":2,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Junjue-Wang/LoveDA","Junjue-Wang/LoveNAS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/vehicle-speed-estimation-using-computer","slug":"vehicle-speed-estimation-using-computer","title":"Vehicle Speed Estimation Using Computer Vision And Evolutionary Camera Calibration","date":"2021-10-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"emds-7-environmental-microorganism-image","title":"EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation","date":"2021-10-11","arxiv_id":"2110.07723","n_code_links":0,"syntology":null},{"paper":"/paper/up-to-down-network-fusing-multi-scale-context","slug":"up-to-down-network-fusing-multi-scale-context","title":"Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion","date":"2021-09-27","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-real-time-and-high-precision-method-for","slug":"a-real-time-and-high-precision-method-for","title":"A real-time and high-precision method for small traffic-signs recognition","date":"2021-09-25","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"psychological-dimension-of-adaptive-trading","title":"Psychological dimension of adaptive trading in cryptocurrency markets","date":"2021-09-24","arxiv_id":"2109.12166","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-dataset-generation-for-bridge-game","title":"Training dataset generation for bridge game registration","date":"2021-09-24","arxiv_id":"2109.11861","n_code_links":0,"syntology":null},{"paper":"/paper/ms-sincresnet-joint-learning-of-1d-and-2d","slug":"ms-sincresnet-joint-learning-of-1d-and-2d","title":"MS-SincResNet: Joint learning of 1D and 2D kernels using multi-scale SincNet and ResNet for music genre classification","date":"2021-09-18","arxiv_id":"2109.08910","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-the-single-shot-multibox-detector","title":"Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse","date":"2021-09-02","arxiv_id":"2109.00810","n_code_links":0,"syntology":null},{"paper":null,"slug":"geometry-based-machining-feature-retrieval","title":"Geometry Based Machining Feature Retrieval with Inductive Transfer Learning","date":"2021-08-26","arxiv_id":"2108.11838","n_code_links":0,"syntology":null},{"paper":"/paper/towards-deep-and-efficient-a-deep-siamese","slug":"towards-deep-and-efficient-a-deep-siamese","title":"Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images","date":"2021-08-18","arxiv_id":"2108.08157","n_code_links":1,"syntology":null},{"paper":"/paper/transductive-few-shot-classification-on-the","slug":"transductive-few-shot-classification-on-the","title":"Transductive Few-Shot Classification on the Oblique Manifold","date":"2021-08-09","arxiv_id":"2108.04009","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["GuodongQi/FSL-OM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"developing-a-compressed-object-detection","title":"Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System","date":"2021-08-01","arxiv_id":"2108.00392","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-pear-fruit-detection-and-counting","slug":"real-time-pear-fruit-detection-and-counting","title":"Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT","date":"2021-07-14","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"real-time-pothole-detection-using-deep","title":"Real-Time Pothole Detection Using Deep Learning","date":"2021-07-13","arxiv_id":"2107.06356","n_code_links":0,"syntology":null},{"paper":"/paper/divergentnets-medical-image-segmentation-by","slug":"divergentnets-medical-image-segmentation-by","title":"DivergentNets: Medical Image Segmentation by Network Ensemble","date":"2021-07-01","arxiv_id":"2107.00283","n_code_links":1,"syntology":null},{"paper":null,"slug":"achieving-real-time-object-detection-on","title":"Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search","date":"2021-06-28","arxiv_id":"2106.14943","n_code_links":0,"syntology":null},{"paper":null,"slug":"small-object-detection-for-near-real-time","title":"Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario","date":"2021-06-11","arxiv_id":"2106.06403","n_code_links":0,"syntology":null},{"paper":"/paper/reducing-the-feature-divergence-of-rgb-and","slug":"reducing-the-feature-divergence-of-rgb-and","title":"Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization","date":"2021-06-06","arxiv_id":"2106.03088","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-scale-feature-aggregation-by-cross","title":"Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation","date":"2021-06-03","arxiv_id":"2106.01744","n_code_links":0,"syntology":null},{"paper":"/paper/multiscale-domain-adaptive-yolo-for-cross","slug":"multiscale-domain-adaptive-yolo-for-cross","title":"Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection","date":"2021-06-02","arxiv_id":"2106.01483","n_code_links":1,"syntology":null},{"paper":"/paper/active-terahertz-imaging-dataset-for","slug":"active-terahertz-imaging-dataset-for","title":"Active Terahertz Imaging Dataset for Concealed Object Detection","date":"2021-05-08","arxiv_id":"2105.03677","n_code_links":1,"syntology":null},{"paper":"/paper/deepplastic-a-novel-approach-to-detecting","slug":"deepplastic-a-novel-approach-to-detecting","title":"A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models","date":"2021-05-05","arxiv_id":"2105.01882","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-data-augmentation-for-object","title":"Unsupervised data augmentation for object detection","date":"2021-04-30","arxiv_id":"2104.14965","n_code_links":0,"syntology":null},{"paper":"/paper/pp-yolov2-a-practical-object-detector","slug":"pp-yolov2-a-practical-object-detector","title":"PP-YOLOv2: A Practical Object Detector","date":"2021-04-21","arxiv_id":"2104.10419","n_code_links":1,"syntology":null},{"paper":null,"slug":"research-on-optimization-method-of-multi","title":"Research on Optimization Method of Multi-scale Fish Target Fast Detection Network","date":"2021-04-11","arxiv_id":"2104.05050","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-fully-automated-end-to-end-process-for","title":"A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification","date":"2021-04-06","arxiv_id":"2104.02793","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-class-motion-based-semantic","title":"Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy","date":"2021-04-02","arxiv_id":"2104.01268","n_code_links":0,"syntology":null},{"paper":"/paper/fakemix-augmentation-improves-transparent","slug":"fakemix-augmentation-improves-transparent","title":"FakeMix Augmentation Improves Transparent Object Detection","date":"2021-03-24","arxiv_id":"2103.13279","n_code_links":1,"syntology":null},{"paper":"/paper/dilated-spinenet-for-semantic-segmentation","slug":"dilated-spinenet-for-semantic-segmentation","title":"Dilated SpineNet for Semantic Segmentation","date":"2021-03-23","arxiv_id":"2103.12270","n_code_links":0,"syntology":null},{"paper":"/paper/control-distance-iou-and-control-distance-iou","slug":"control-distance-iou-and-control-distance-iou","title":"Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression","date":"2021-03-22","arxiv_id":"2103.11696","n_code_links":1,"syntology":null},{"paper":"/paper/you-only-look-one-level-feature","slug":"you-only-look-one-level-feature","title":"You Only Look One-level Feature","date":"2021-03-17","arxiv_id":"2103.09460","n_code_links":6,"syntology":null},{"paper":null,"slug":"pavement-distress-detection-and-segmentation","title":"Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines","date":"2021-03-11","arxiv_id":"2103.06467","n_code_links":0,"syntology":null},{"paper":null,"slug":"sar-u-net-squeeze-and-excitation-block-and","title":"SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography","date":"2021-03-11","arxiv_id":"2103.06419","n_code_links":0,"syntology":null},{"paper":"/paper/categorical-depth-distribution-network-for","slug":"categorical-depth-distribution-network-for","title":"Categorical Depth Distribution Network for Monocular 3D Object Detection","date":"2021-03-01","arxiv_id":"2103.01100","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["TRAILab/CaDDN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"coarse-to-fine-airway-segmentation-using","title":"Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing","date":"2021-02-25","arxiv_id":"2102.12755","n_code_links":0,"syntology":null},{"paper":"/paper/image-augmentation-for-multitask-few-shot","slug":"image-augmentation-for-multitask-few-shot","title":"Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case","date":"2021-02-24","arxiv_id":"2102.12295","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-gan-based-input-size-flexibility-model-for","title":"A GAN-Based Input-Size Flexibility Model for Single Image Dehazing","date":"2021-02-19","arxiv_id":"2102.09796","n_code_links":0,"syntology":null},{"paper":"/paper/fast-accurate-barcode-detection-in-ultra-high","slug":"fast-accurate-barcode-detection-in-ultra-high","title":"Fast, Accurate Barcode Detection in Ultra High-Resolution Images","date":"2021-02-13","arxiv_id":"2102.06868","n_code_links":0,"syntology":null},{"paper":"/paper/broad-unet-multi-scale-feature-learning-for","slug":"broad-unet-multi-scale-feature-learning-for","title":"Broad-UNet: Multi-scale feature learning for nowcasting tasks","date":"2021-02-12","arxiv_id":"2102.06442","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-semantic-segmentation-by","slug":"unsupervised-semantic-segmentation-by","title":"Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals","date":"2021-02-11","arxiv_id":"2102.06191","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["wvangansbeke/Unsupervised-Semantic-Segmentation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/prototypical-pseudo-label-denoising-and","slug":"prototypical-pseudo-label-denoising-and","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","date":"2021-01-26","arxiv_id":"2101.10979","n_code_links":2,"syntology":{"ran":8,"of":17,"n_ran_checked":6,"n_instrument":2,"unverified":9,"pointer_only":0,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","official":{"repos":["microsoft/ProDA"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"more-reliable-ai-solution-breast-ultrasound","title":"More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination","date":"2021-01-07","arxiv_id":"2101.02639","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-adversarial-attack-to-object-detection","slug":"sparse-adversarial-attack-to-object-detection","title":"Sparse Adversarial Attack to Object Detection","date":"2020-12-26","arxiv_id":"2012.13692","n_code_links":1,"syntology":null},{"paper":"/paper/aerial-imagery-pixel-level-segmentation","slug":"aerial-imagery-pixel-level-segmentation","title":"Aerial Imagery Pixel-level Segmentation","date":"2020-12-03","arxiv_id":"2012.02024","n_code_links":1,"syntology":null},{"paper":null,"slug":"traffic-surveillance-using-vehicle-license","title":"Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh","date":"2020-12-03","arxiv_id":"2012.02218","n_code_links":0,"syntology":null},{"paper":"/paper/learning-universal-shape-dictionary-for","slug":"learning-universal-shape-dictionary-for","title":"Learning Universal Shape Dictionary for Realtime Instance Segmentation","date":"2020-12-02","arxiv_id":"2012.01050","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-multi-scale-features-learning-for","title":"Deep Multi-Scale Features Learning for Distorted Image Quality Assessment","date":"2020-12-01","arxiv_id":"2012.01980","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-detection-of-cardiac-chambers-using","title":"Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography","date":"2020-11-26","arxiv_id":"2011.13096","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-approach-on-detecting-multi","title":"A comparative approach on detecting multi-lingual and multi-oriented text in natural scene images","date":"2020-11-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/scaled-yolov4-scaling-cross-stage-partial","slug":"scaled-yolov4-scaling-cross-stage-partial","title":"Scaled-YOLOv4: Scaling Cross Stage Partial Network","date":"2020-11-16","arxiv_id":"2011.08036","n_code_links":41,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["WongKinYiu/ScaledYOLOv4"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}}],"record_sha256":"ddfb10e0b009eb3ab5a60fcc8045e08262493fde5a4559743092d26d8d3a3da9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}