{"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/non-maximum-suppression/papers/3","list_of":"/method/non-maximum-suppression","method":"Non Maximum Suppression","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":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":389,"counts":{"archive_papers_tagged":389,"with_a_code_link":174,"where_syntology_ran_a_sample":40,"not_listed_spam_title":0,"listed":389,"listed_where_code_ran":40,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":32,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":32,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/non-maximum-suppression","prev":"/method/non-maximum-suppression/papers/2","next":"/method/non-maximum-suppression/papers/4","papers":[{"paper":"/paper/anchor-free-person-search","slug":"anchor-free-person-search","title":"Anchor-Free Person Search","date":"2021-03-22","arxiv_id":"2103.11617","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["daodaofr/AlignPS"],"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/ssd-a-unified-framework-for-self-supervised-1","slug":"ssd-a-unified-framework-for-self-supervised-1","title":"SSD: A Unified Framework for Self-Supervised Outlier Detection","date":"2021-03-22","arxiv_id":"2103.12051","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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":["inspire-group/SSD"],"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":"modulating-localization-and-classification","title":"Modulating Localization and Classification for Harmonized Object Detection","date":"2021-03-16","arxiv_id":"2103.08958","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-versus-mathematical-model-to","title":"Machine Learning versus Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel","date":"2021-03-06","arxiv_id":"2103.05447","n_code_links":0,"syntology":null},{"paper":"/paper/over-sampling-de-occlusion-attention-network","slug":"over-sampling-de-occlusion-attention-network","title":"Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images","date":"2021-03-01","arxiv_id":"2103.00809","n_code_links":1,"syntology":null},{"paper":"/paper/acdnet-an-action-detection-network-for-real","slug":"acdnet-an-action-detection-network-for-real","title":"ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation","date":"2021-02-26","arxiv_id":"2102.13493","n_code_links":1,"syntology":null},{"paper":null,"slug":"visual-diagnosis-of-the-varroa-destructor","title":"Visual diagnosis of the Varroa destructor parasitic mite in honeybees using object detector techniques","date":"2021-02-26","arxiv_id":"2103.03133","n_code_links":0,"syntology":null},{"paper":null,"slug":"aboships-an-inshore-and-offshore-maritime","title":"ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations","date":"2021-02-11","arxiv_id":"2102.05869","n_code_links":0,"syntology":null},{"paper":"/paper/active-slices-for-sliced-stein-discrepancy","slug":"active-slices-for-sliced-stein-discrepancy","title":"Active Slices for Sliced Stein Discrepancy","date":"2021-02-05","arxiv_id":"2102.03159","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["WenboGong/Sliced_Kernelized_Stein_Discrepancy"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"recssd-near-data-processing-for-solid-state","title":"RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference","date":"2021-01-29","arxiv_id":"2102.00075","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-made-simpler-by-eliminating","title":"Object Detection Made Simpler by Eliminating Heuristic NMS","date":"2021-01-28","arxiv_id":"2101.11782","n_code_links":0,"syntology":null},{"paper":"/paper/towards-robust-visual-information-extraction","slug":"towards-robust-visual-information-extraction","title":"Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution","date":"2021-01-24","arxiv_id":"2102.06732","n_code_links":1,"syntology":null},{"paper":"/paper/acp-automatic-channel-pruning-via-clustering","slug":"acp-automatic-channel-pruning-via-clustering","title":"ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN","date":"2021-01-16","arxiv_id":"2101.06407","n_code_links":1,"syntology":null},{"paper":"/paper/lla-loss-aware-label-assignment-for-dense","slug":"lla-loss-aware-label-assignment-for-dense","title":"LLA: Loss-aware Label Assignment for Dense Pedestrian Detection","date":"2021-01-12","arxiv_id":"2101.04307","n_code_links":1,"syntology":null},{"paper":null,"slug":"polarnet-learning-to-optimize-polar-keypoints","title":"PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-feature-pyramid-network-for-object","title":"Implicit Feature Pyramid Network for Object Detection","date":"2020-12-25","arxiv_id":"2012.13563","n_code_links":0,"syntology":null},{"paper":"/paper/swa-object-detection","slug":"swa-object-detection","title":"SWA Object Detection","date":"2020-12-23","arxiv_id":"2012.12645","n_code_links":2,"syntology":null},{"paper":"/paper/efficient-golf-ball-detection-and-tracking","slug":"efficient-golf-ball-detection-and-tracking","title":"Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter","date":"2020-12-17","arxiv_id":"2012.09393","n_code_links":1,"syntology":null},{"paper":null,"slug":"tdaf-top-down-attention-framework-for-vision","title":"TDAF: Top-Down Attention Framework for Vision Tasks","date":"2020-12-14","arxiv_id":"2012.07248","n_code_links":0,"syntology":null},{"paper":null,"slug":"monetary-risk-measures","title":"Monetary Risk Measures","date":"2020-12-12","arxiv_id":"2012.06751","n_code_links":0,"syntology":null},{"paper":"/paper/co-mining-self-supervised-learning-for","slug":"co-mining-self-supervised-learning-for","title":"Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection","date":"2020-12-03","arxiv_id":"2012.01950","n_code_links":1,"syntology":null},{"paper":null,"slug":"dual-refinement-feature-pyramid-networks-for","title":"Dual Refinement Feature Pyramid Networks for Object Detection","date":"2020-12-03","arxiv_id":"2012.01733","n_code_links":0,"syntology":null},{"paper":"/paper/dataset-for-eye-tracking-tasks","slug":"dataset-for-eye-tracking-tasks","title":"Dataset for eye-tracking tasks","date":"2020-12-02","arxiv_id":null,"n_code_links":1,"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":"an-analysis-of-deep-object-detectors-for","title":"An Analysis of Deep Object Detectors For Diver Detection","date":"2020-11-25","arxiv_id":"2012.05701","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-algorithms-for-convex-nested","title":"Optimal Algorithms for Convex Nested Stochastic Composite Optimization","date":"2020-11-19","arxiv_id":"2011.10076","n_code_links":0,"syntology":null},{"paper":null,"slug":"unifying-instance-and-panoptic-segmentation","title":"Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions","date":"2020-11-19","arxiv_id":"2011.09796","n_code_links":0,"syntology":null},{"paper":"/paper/tju-dhd-a-diverse-high-resolution-dataset-for","slug":"tju-dhd-a-diverse-high-resolution-dataset-for","title":"TJU-DHD: A Diverse High-Resolution Dataset for Object Detection","date":"2020-11-18","arxiv_id":"2011.09170","n_code_links":1,"syntology":null},{"paper":null,"slug":"phoebe-reuse-aware-online-caching-with","title":"Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models","date":"2020-11-13","arxiv_id":"2011.07160","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-object-detection-with-latticed-multi","title":"Fast Object Detection with Latticed Multi-Scale Feature Fusion","date":"2020-11-05","arxiv_id":"2011.02780","n_code_links":0,"syntology":null},{"paper":"/paper/smot-single-shot-multi-object-tracking","slug":"smot-single-shot-multi-object-tracking","title":"SMOT: Single-Shot Multi Object Tracking","date":"2020-10-30","arxiv_id":"2010.16031","n_code_links":1,"syntology":null},{"paper":null,"slug":"black-box-optimization-of-object-detector","title":"Black-Box Optimization of Object Detector Scales","date":"2020-10-29","arxiv_id":"2010.15823","n_code_links":0,"syntology":null},{"paper":"/paper/relationnet-bridging-visual-representations","slug":"relationnet-bridging-visual-representations","title":"RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder","date":"2020-10-29","arxiv_id":"2010.15831","n_code_links":4,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["microsoft/RelationNet2"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"real-time-mask-detection-on-google-edge-tpu","title":"Real-time Mask Detection on Google Edge TPU","date":"2020-10-09","arxiv_id":"2010.04427","n_code_links":0,"syntology":null},{"paper":null,"slug":"uesegnet-context-aware-unconstrained-roi","title":"UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric","date":"2020-10-08","arxiv_id":"2010.03990","n_code_links":0,"syntology":null},{"paper":"/paper/a-mobile-app-for-wound-localization-using","slug":"a-mobile-app-for-wound-localization-using","title":"A Mobile App for Wound Localization using Deep Learning","date":"2020-09-15","arxiv_id":"2009.07133","n_code_links":1,"syntology":null},{"paper":null,"slug":"modification-method-for-single-stage-object","title":"Modification method for single-stage object detectors that allows to exploit the temporal behaviour of a scene to improve detection accuracy","date":"2020-09-03","arxiv_id":"2009.01617","n_code_links":0,"syntology":null},{"paper":"/paper/varifocalnet-an-iou-aware-dense-object","slug":"varifocalnet-an-iou-aware-dense-object","title":"VarifocalNet: An IoU-aware Dense Object Detector","date":"2020-08-31","arxiv_id":"2008.13367","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hyz-xmaster/VarifocalNet"],"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":["official"]}}},{"paper":null,"slug":"object-detection-in-the-context-of-mobile","title":"Object Detection in the Context of Mobile Augmented Reality","date":"2020-08-15","arxiv_id":"2008.06655","n_code_links":0,"syntology":null},{"paper":"/paper/borderdet-border-feature-for-dense-object","slug":"borderdet-border-feature-for-dense-object","title":"BorderDet: Border Feature for Dense Object Detection","date":"2020-07-21","arxiv_id":"2007.11056","n_code_links":2,"syntology":null},{"paper":"/paper/asap-nms-accelerating-non-maximum-suppression","slug":"asap-nms-accelerating-non-maximum-suppression","title":"ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors","date":"2020-07-19","arxiv_id":"2007.09785","n_code_links":1,"syntology":null},{"paper":"/paper/aqd-towards-accurate-quantized-object","slug":"aqd-towards-accurate-quantized-object","title":"AQD: Towards Accurate Fully-Quantized Object Detection","date":"2020-07-14","arxiv_id":"2007.06919","n_code_links":1,"syntology":null},{"paper":"/paper/understanding-object-detection-through-an","slug":"understanding-object-detection-through-an","title":"Understanding Object Detection Through An Adversarial Lens","date":"2020-07-11","arxiv_id":"2007.05828","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-switch-cnns-with-model-agnostic","title":"Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing","date":"2020-07-09","arxiv_id":"2007.04645","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-evaluation-of-object-detection","title":"A Systematic Evaluation of Object Detection Networks for Scientific Plots","date":"2020-07-05","arxiv_id":"2007.02240","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-shot-3d-detection-of-vehicles-from","title":"Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time","date":"2020-06-23","arxiv_id":"2006.13084","n_code_links":0,"syntology":null},{"paper":null,"slug":"visibility-guided-nms-efficient-boosting-of","title":"Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes","date":"2020-06-15","arxiv_id":"2006.08547","n_code_links":0,"syntology":null},{"paper":"/paper/fcos-a-simple-and-strong-anchor-free-object","slug":"fcos-a-simple-and-strong-anchor-free-object","title":"FCOS: A simple and strong anchor-free object detector","date":"2020-06-14","arxiv_id":"2006.09214","n_code_links":1,"syntology":null},{"paper":"/paper/tensorflow-with-user-friendly-graphical","slug":"tensorflow-with-user-friendly-graphical","title":"TensorFlow with user friendly Graphical Framework for object detection API","date":"2020-06-11","arxiv_id":"2006.06385","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-neural-network-based-real-time-kiwi","title":"Deep Neural Network Based Real-time Kiwi Fruit Flower Detection in an Orchard Environment","date":"2020-06-08","arxiv_id":"2006.04343","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthesizing-unrestricted-false-positive","title":"Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models","date":"2020-05-19","arxiv_id":"2005.09294","n_code_links":0,"syntology":null},{"paper":null,"slug":"parallel-learning-of-koopman-eigenfunctions","title":"Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction","date":"2020-05-13","arxiv_id":"2005.06138","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-geometric-factors-in-model-learning","slug":"enhancing-geometric-factors-in-model-learning","title":"Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation","date":"2020-05-07","arxiv_id":"2005.03572","n_code_links":6,"syntology":{"ran":2,"of":5,"n_ran_checked":1,"n_instrument":1,"unverified":3,"pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["Zzh-tju/CIoU"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"seismic-shot-gather-noise-localization-using","title":"Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network","date":"2020-05-07","arxiv_id":"2005.03626","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-vehicle-object-detection-in-the-wild-for","title":"In-Vehicle Object Detection in the Wild for Driverless Vehicles","date":"2020-04-27","arxiv_id":"2004.12700","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-safety-of-vulnerable-road-users-by","slug":"on-the-safety-of-vulnerable-road-users-by","title":"On the safety of vulnerable road users by cyclist orientation detection using Deep Learning","date":"2020-04-25","arxiv_id":"2004.11909","n_code_links":0,"syntology":null},{"paper":"/paper/occluded-prohibited-items-detection-an-x-ray","slug":"occluded-prohibited-items-detection-an-x-ray","title":"Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module","date":"2020-04-18","arxiv_id":"2004.08656","n_code_links":2,"syntology":null},{"paper":"/paper/dynamic-r-cnn-towards-high-quality-object","slug":"dynamic-r-cnn-towards-high-quality-object","title":"Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training","date":"2020-04-13","arxiv_id":"2004.06002","n_code_links":3,"syntology":{"ran":9,"of":18,"n_ran_checked":9,"n_instrument":0,"unverified":9,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","official":{"repos":["hkzhang95/DynamicRCNN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"centermask-single-shot-instance-segmentation","title":"CenterMask: single shot instance segmentation with point representation","date":"2020-04-09","arxiv_id":"2004.04446","n_code_links":0,"syntology":null},{"paper":null,"slug":"tracking-by-instance-detection-a-meta","title":"Tracking by Instance Detection: A Meta-Learning Approach","date":"2020-04-02","arxiv_id":"2004.00830","n_code_links":0,"syntology":null},{"paper":"/paper/hit-detector-hierarchical-trinity","slug":"hit-detector-hierarchical-trinity","title":"Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection","date":"2020-03-26","arxiv_id":"2003.11818","n_code_links":1,"syntology":null},{"paper":null,"slug":"identifying-individual-dogs-in-social-media","title":"Identifying Individual Dogs in Social Media Images","date":"2020-03-14","arxiv_id":"2003.06705","n_code_links":0,"syntology":null},{"paper":null,"slug":"pointins-point-based-instance-segmentation","title":"PointINS: Point-based Instance Segmentation","date":"2020-03-13","arxiv_id":"2003.06148","n_code_links":0,"syntology":null},{"paper":"/paper/distributed-hierarchical-gpu-parameter-server","slug":"distributed-hierarchical-gpu-parameter-server","title":"Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems","date":"2020-03-12","arxiv_id":"2003.05622","n_code_links":2,"syntology":null},{"paper":null,"slug":"online-self-supervised-learning-for-object","title":"Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach","date":"2020-03-08","arxiv_id":"2003.03717","n_code_links":0,"syntology":null},{"paper":null,"slug":"target-detection-tracking-and-avoidance","title":"Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches","date":"2020-02-27","arxiv_id":"2002.12461","n_code_links":0,"syntology":null},{"paper":null,"slug":"selective-convolutional-network-an-efficient","title":"Selective Convolutional Network: An Efficient Object Detector with Ignoring Background","date":"2020-02-04","arxiv_id":"2002.01205","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-inspection-system-for-variable-data","title":"A Novel Inspection System For Variable Data Printing Using Deep Learning","date":"2020-01-13","arxiv_id":"2001.04325","n_code_links":0,"syntology":null},{"paper":"/paper/blendmask-top-down-meets-bottom-up-for","slug":"blendmask-top-down-meets-bottom-up-for","title":"BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation","date":"2020-01-02","arxiv_id":"2001.00309","n_code_links":9,"syntology":null},{"paper":null,"slug":"small-object-detection-using-context-and","title":"Small Object Detection using Context and Attention","date":"2019-12-13","arxiv_id":"1912.06319","n_code_links":0,"syntology":null},{"paper":"/paper/the-benefits-of-close-domain-fine-tuning-for","slug":"the-benefits-of-close-domain-fine-tuning-for","title":"The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images","date":"2019-12-12","arxiv_id":"1912.05846","n_code_links":1,"syntology":null},{"paper":"/paper/augfpn-improving-multi-scale-feature-learning","slug":"augfpn-improving-multi-scale-feature-learning","title":"AugFPN: Improving Multi-scale Feature Learning for Object Detection","date":"2019-12-11","arxiv_id":"1912.05384","n_code_links":2,"syntology":null},{"paper":"/paper/footandball-integrated-player-and-ball","slug":"footandball-integrated-player-and-ball","title":"FootAndBall: Integrated player and ball detector","date":"2019-12-10","arxiv_id":"1912.05445","n_code_links":1,"syntology":null},{"paper":"/paper/bridging-the-gap-between-anchor-based-and","slug":"bridging-the-gap-between-anchor-based-and","title":"Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection","date":"2019-12-05","arxiv_id":"1912.02424","n_code_links":13,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sfzhang15/ATSS"],"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/learning-spatial-fusion-for-single-shot","slug":"learning-spatial-fusion-for-single-shot","title":"Learning Spatial Fusion for Single-Shot Object Detection","date":"2019-11-21","arxiv_id":"1911.09516","n_code_links":1,"syntology":null},{"paper":"/paper/distance-iou-loss-faster-and-better-learning","slug":"distance-iou-loss-faster-and-better-learning","title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","date":"2019-11-19","arxiv_id":"1911.08287","n_code_links":20,"syntology":{"ran":17,"of":25,"n_ran_checked":14,"n_instrument":3,"unverified":8,"pointer_only":6,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","official":{"repos":["Zzh-tju/DIoU"],"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":["listed","unlocated"]}}},{"paper":"/paper/centermask-real-time-anchor-free-instance-1","slug":"centermask-real-time-anchor-free-instance-1","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","date":"2019-11-15","arxiv_id":"1911.06667","n_code_links":8,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["youngwanLEE/CenterMask"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"localization-aware-channel-pruning-for-object","title":"Localization-aware Channel Pruning for Object Detection","date":"2019-11-06","arxiv_id":"1911.02237","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparable-study-intrinsic-difficulties-of","title":"A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images","date":"2019-10-25","arxiv_id":"1910.11506","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-performance-envelope-of-inverted-indexing","title":"The Performance Envelope of Inverted Indexing on Modern Hardware","date":"2019-10-24","arxiv_id":"1910.11028","n_code_links":0,"syntology":null},{"paper":null,"slug":"kidney-recognition-in-ct-using-yolov3","title":"Kidney Recognition in CT Using YOLOv3","date":"2019-10-03","arxiv_id":"1910.01268","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-gap-between-detection-and","title":"Bridging the Gap Between Detection and Tracking: A Unified Approach","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-fusion-detector-for-semantic","title":"Feature Fusion Detector for Semantic Cognition of Remote Sensing","date":"2019-09-28","arxiv_id":"1909.13047","n_code_links":0,"syntology":null},{"paper":"/paper/assd-attentive-single-shot-multibox-detector","slug":"assd-attentive-single-shot-multibox-detector","title":"ASSD: Attentive Single Shot Multibox Detector","date":"2019-09-27","arxiv_id":"1909.12456","n_code_links":1,"syntology":null},{"paper":"/paper/balanced-binary-neural-networks-with-gated","slug":"balanced-binary-neural-networks-with-gated","title":"Balanced Binary Neural Networks with Gated Residual","date":"2019-09-26","arxiv_id":"1909.12117","n_code_links":1,"syntology":null},{"paper":null,"slug":"190909945","title":"To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?","date":"2019-09-22","arxiv_id":"1909.09945","n_code_links":0,"syntology":null},{"paper":null,"slug":"190909756","title":"Scale MLPerf-0.6 models on Google TPU-v3 Pods","date":"2019-09-21","arxiv_id":"1909.09756","n_code_links":0,"syntology":null},{"paper":"/paper/gaussian-temporal-awareness-networks-for-1","slug":"gaussian-temporal-awareness-networks-for-1","title":"Gaussian Temporal Awareness Networks for Action Localization","date":"2019-09-09","arxiv_id":"1909.03877","n_code_links":1,"syntology":null},{"paper":"/paper/freeanchor-learning-to-match-anchors-for","slug":"freeanchor-learning-to-match-anchors-for","title":"FreeAnchor: Learning to Match Anchors for Visual Object Detection","date":"2019-09-05","arxiv_id":"1909.02466","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhangxiaosong18/FreeAnchor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/machine-learning-approach-of-automatic","slug":"machine-learning-approach-of-automatic","title":"Machine learning approach of automatic identification and counting of blood cells","date":"2019-09-05","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/fast-and-efficient-model-for-real-time-tiger","slug":"fast-and-efficient-model-for-real-time-tiger","title":"Fast and Efficient Model for Real-Time Tiger Detection In The Wild","date":"2019-09-03","arxiv_id":"1909.01122","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-stream-single-shot-spatial-temporal","title":"Multi-Stream Single Shot Spatial-Temporal Action Detection","date":"2019-08-22","arxiv_id":"1908.08178","n_code_links":0,"syntology":null},{"paper":"/paper/semi-automatic-labeling-for-deep-learning-in","slug":"semi-automatic-labeling-for-deep-learning-in","title":"Semi-Automatic Labeling for Deep Learning in Robotics","date":"2019-08-05","arxiv_id":"1908.01862","n_code_links":1,"syntology":null},{"paper":null,"slug":"propose-and-attend-single-shot-detector","title":"Propose-and-Attend Single Shot Detector","date":"2019-07-30","arxiv_id":"1907.12736","n_code_links":0,"syntology":null},{"paper":"/paper/compact-global-descriptor-for-neural-networks","slug":"compact-global-descriptor-for-neural-networks","title":"Compact Global Descriptor for Neural Networks","date":"2019-07-23","arxiv_id":"1907.09665","n_code_links":1,"syntology":null},{"paper":null,"slug":"searching-for-apparel-products-from-images-in","title":"Searching for Apparel Products from Images in the Wild","date":"2019-07-04","arxiv_id":"1907.02244","n_code_links":0,"syntology":null},{"paper":"/paper/nas-fcos-fast-neural-architecture-search-for","slug":"nas-fcos-fast-neural-architecture-search-for","title":"NAS-FCOS: Fast Neural Architecture Search for Object Detection","date":"2019-06-11","arxiv_id":"1906.04423","n_code_links":3,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/dicenet-dimension-wise-convolutions-for","slug":"dicenet-dimension-wise-convolutions-for","title":"DiCENet: Dimension-wise Convolutions for Efficient Networks","date":"2019-06-08","arxiv_id":"1906.03516","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sacmehta/EdgeNets"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"group-sampling-for-scale-invariant-face","title":"Group Sampling for Scale Invariant Face Detection","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"distant-pedestrian-detection-in-the-wild","title":"Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks","date":"2019-05-29","arxiv_id":"1905.12759","n_code_links":0,"syntology":null}],"record_sha256":"8115c88077847d96ed7de0b141cad64380d5af63d8ee98a9872f4b01b711f429","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}