{"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/object-detection-1/papers/8","list_of":"/task/object-detection-1","task":"object-detection","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":8,"pages_in_order":106,"rows_per_page":100,"rows":[701,800],"of":10514,"counts":{"archive_papers_tagged":10514,"with_a_code_link":4285,"where_syntology_ran_a_sample":1027,"not_listed_spam_title":0,"listed":10514,"listed_where_code_ran":1027,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":898,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":898,"listed_every_run_a_failure_of_syntologys_instrument":129,"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/object-detection-1","prev":"/task/object-detection-1/papers/7","next":"/task/object-detection-1/papers/9","papers":[{"url":"/paper/dynamic-anchor-learning-for-arbitrary","slug":"dynamic-anchor-learning-for-arbitrary","title":"Dynamic Anchor Learning for Arbitrary-Oriented Object Detection","date":"2020-12-08","arxiv_id":"2012.04150","repositories_listed":2,"syntology":null},{"url":"/paper/real-time-gun-detection-in-cctv-an-open","slug":"real-time-gun-detection-in-cctv-an-open","title":"Real-time gun detection in CCTV: An open problem","date":"2020-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/superloss-a-generic-loss-for-robust","slug":"superloss-a-generic-loss-for-robust","title":"SuperLoss: A Generic Loss for Robust Curriculum Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/tinaface-strong-but-simple-baseline-for-face","slug":"tinaface-strong-but-simple-baseline-for-face","title":"TinaFace: Strong but Simple Baseline for Face Detection","date":"2020-11-26","arxiv_id":"2011.13183","repositories_listed":2,"syntology":null},{"url":"/paper/one-metric-to-measure-them-all-localisation","slug":"one-metric-to-measure-them-all-localisation","title":"One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks","date":"2020-11-21","arxiv_id":"2011.10772","repositories_listed":2,"syntology":null},{"url":"/paper/up-detr-unsupervised-pre-training-for-object","slug":"up-detr-unsupervised-pre-training-for-object","title":"UP-DETR: Unsupervised Pre-training for Object Detection with Transformers","date":"2020-11-18","arxiv_id":"2011.09094","repositories_listed":2,"syntology":null},{"url":"/paper/cream-of-the-crop-distilling-prioritized","slug":"cream-of-the-crop-distilling-prioritized","title":"Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search","date":"2020-10-29","arxiv_id":"2010.15821","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/cream-of-the-crop-distilling-prioritized#ran","syntology_url":"https://syntology.ai/paper/2010.15821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15821"}},"official":{"repos":["microsoft/cream"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/r-tod-real-time-object-detector-with","slug":"r-tod-real-time-object-detector-with","title":"R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving","date":"2020-10-23","arxiv_id":"2011.06372","repositories_listed":2,"syntology":null},{"url":"/paper/hs-resnet-hierarchical-split-block-on","slug":"hs-resnet-hierarchical-split-block-on","title":"HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network","date":"2020-10-15","arxiv_id":"2010.07621","repositories_listed":2,"syntology":null},{"url":"/paper/mlod-awareness-of-extrinsic-perturbation-in","slug":"mlod-awareness-of-extrinsic-perturbation-in","title":"MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving","date":"2020-09-29","arxiv_id":"2010.11702","repositories_listed":2,"syntology":null},{"url":"/paper/relativenas-relative-neural-architecture","slug":"relativenas-relative-neural-architecture","title":"RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning","date":"2020-09-14","arxiv_id":"2009.06193","repositories_listed":2,"syntology":null},{"url":"/paper/monitoring-spatial-sustainable-development-1","slug":"monitoring-spatial-sustainable-development-1","title":"Monitoring Spatial Sustainable Development: semi-automated analysis of Satellite and Aerial Images for Energy Transition and Sustainability Indicators","date":"2020-09-12","arxiv_id":"2009.05738","repositories_listed":2,"syntology":null},{"url":"/paper/siamese-network-for-rgb-d-salient-object","slug":"siamese-network-for-rgb-d-salient-object","title":"Siamese Network for RGB-D Salient Object Detection and Beyond","date":"2020-08-26","arxiv_id":"2008.12134","repositories_listed":2,"syntology":null},{"url":"/paper/deformable-pv-rcnn-improving-3d-object","slug":"deformable-pv-rcnn-improving-3d-object","title":"Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations","date":"2020-08-20","arxiv_id":"2008.08766","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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","sample_list":"/paper/deformable-pv-rcnn-improving-3d-object#ran","syntology_url":"https://syntology.ai/paper/2008.08766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08766"}},"official":{"repos":["AutoVision-cloud/Deformable-PV-RCNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/tide-a-general-toolbox-for-identifying-object","slug":"tide-a-general-toolbox-for-identifying-object","title":"TIDE: A General Toolbox for Identifying Object Detection Errors","date":"2020-08-18","arxiv_id":"2008.08115","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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","sample_list":"/paper/tide-a-general-toolbox-for-identifying-object#ran","syntology_url":"https://syntology.ai/paper/2008.08115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08115"}},"official":{"repos":["dbolya/tide"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/object-detection-for-graphical-user-interface","slug":"object-detection-for-graphical-user-interface","title":"Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?","date":"2020-08-12","arxiv_id":"2008.05132","repositories_listed":2,"syntology":null},{"url":"/paper/multiple-instance-learning-on-deep-features","slug":"multiple-instance-learning-on-deep-features","title":"Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts","date":"2020-08-03","arxiv_id":"2008.01178","repositories_listed":2,"syntology":null},{"url":"/paper/neural-compression-and-filtering-for-edge","slug":"neural-compression-and-filtering-for-edge","title":"Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks","date":"2020-07-31","arxiv_id":"2007.15818","repositories_listed":2,"syntology":null},{"url":"/paper/a-self-training-approach-for-point-supervised","slug":"a-self-training-approach-for-point-supervised","title":"A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds","date":"2020-07-25","arxiv_id":"2007.12831","repositories_listed":2,"syntology":null},{"url":"/paper/accurate-rgb-d-salient-object-detection-via","slug":"accurate-rgb-d-salient-object-detection-via","title":"Accurate RGB-D Salient Object Detection via Collaborative Learning","date":"2020-07-23","arxiv_id":"2007.11782","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/accurate-rgb-d-salient-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2007.11782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11782"}},"official":{"repos":["OIPLab-DUT/CoNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/few-shot-object-detection-and-viewpoint","slug":"few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","arxiv_id":"2007.12107","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-object-detection-and-viewpoint#ran","syntology_url":"https://syntology.ai/paper/2007.12107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12107"}},"official":null}},{"url":"/paper/a-survey-on-performance-metrics-for-object","slug":"a-survey-on-performance-metrics-for-object","title":"A Survey on Performance Metrics for Object-Detection Algorithms","date":"2020-07-21","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/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","repositories_listed":2,"syntology":null},{"url":"/paper/kinematic-3d-object-detection-in-monocular","slug":"kinematic-3d-object-detection-in-monocular","title":"Kinematic 3D Object Detection in Monocular Video","date":"2020-07-19","arxiv_id":"2007.09548","repositories_listed":2,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":16,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":2,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/kinematic-3d-object-detection-in-monocular#ran","syntology_url":"https://syntology.ai/paper/2007.09548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09548"}},"official":null}},{"url":"/paper/learning-visual-context-by-comparison","slug":"learning-visual-context-by-comparison","title":"Learning Visual Context by Comparison","date":"2020-07-15","arxiv_id":"2007.07506","repositories_listed":2,"syntology":null},{"url":"/paper/centernet3d-an-anchor-free-object-detector","slug":"centernet3d-an-anchor-free-object-detector","title":"CenterNet3D: An Anchor Free Object Detector for Point Cloud","date":"2020-07-13","arxiv_id":"2007.07214","repositories_listed":2,"syntology":null},{"url":"/paper/cross-modal-weighting-network-for-rgb-d","slug":"cross-modal-weighting-network-for-rgb-d","title":"Cross-Modal Weighting Network for RGB-D Salient Object Detection","date":"2020-07-09","arxiv_id":"2007.04901","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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","sample_list":"/paper/cross-modal-weighting-network-for-rgb-d#ran","syntology_url":"https://syntology.ai/paper/2007.04901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04901"}},"official":{"repos":["MathLee/CMWNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/autoassign-differentiable-label-assignment","slug":"autoassign-differentiable-label-assignment","title":"AutoAssign: Differentiable Label Assignment for Dense Object Detection","date":"2020-07-07","arxiv_id":"2007.03496","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/autoassign-differentiable-label-assignment#ran","syntology_url":"https://syntology.ai/paper/2007.03496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03496"}},"official":{"repos":["Megvii-BaseDetection/AutoAssign"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/re-thinking-co-salient-object-detection","slug":"re-thinking-co-salient-object-detection","title":"Re-thinking Co-Salient Object Detection","date":"2020-07-07","arxiv_id":"2007.03380","repositories_listed":2,"syntology":null},{"url":"/paper/rgbt-salient-object-detection-a-large-scale","slug":"rgbt-salient-object-detection-a-large-scale","title":"RGBT Salient Object Detection: A Large-scale Dataset and Benchmark","date":"2020-07-07","arxiv_id":"2007.03262","repositories_listed":2,"syntology":null},{"url":"/paper/bbs-net-rgb-d-salient-object-detection-with-a","slug":"bbs-net-rgb-d-salient-object-detection-with-a","title":"Bifurcated backbone strategy for RGB-D salient object detection","date":"2020-07-06","arxiv_id":"2007.02713","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"4 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bbs-net-rgb-d-salient-object-detection-with-a#ran","syntology_url":"https://syntology.ai/paper/2007.02713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02713"}},"official":{"repos":["zyjwuyan/BBS-Net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/houghnet-integrating-near-and-long-range","slug":"houghnet-integrating-near-and-long-range","title":"HoughNet: Integrating near and long-range evidence for bottom-up object detection","date":"2020-07-05","arxiv_id":"2007.02355","repositories_listed":2,"syntology":null},{"url":"/paper/fna-fast-network-adaptation-via-parameter","slug":"fna-fast-network-adaptation-via-parameter","title":"FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search","date":"2020-06-21","arxiv_id":"2006.12986","repositories_listed":2,"syntology":null},{"url":"/paper/overcoming-classifier-imbalance-for-long-tail-1","slug":"overcoming-classifier-imbalance-for-long-tail-1","title":"Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax","date":"2020-06-18","arxiv_id":"2006.10408","repositories_listed":2,"syntology":null},{"url":"/paper/codenet-algorithm-hardware-co-design-for","slug":"codenet-algorithm-hardware-co-design-for","title":"CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs","date":"2020-06-12","arxiv_id":"2006.08357","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-pre-training-and-self-training","slug":"rethinking-pre-training-and-self-training","title":"Rethinking Pre-training and Self-training","date":"2020-06-11","arxiv_id":"2006.06882","repositories_listed":2,"syntology":null},{"url":"/paper/h3dnet-3d-object-detection-using-hybrid","slug":"h3dnet-3d-object-detection-using-hybrid","title":"H3DNet: 3D Object Detection Using Hybrid Geometric Primitives","date":"2020-06-10","arxiv_id":"2006.05682","repositories_listed":2,"syntology":null},{"url":"/paper/a-self-supervised-approach-for-adversarial-1","slug":"a-self-supervised-approach-for-adversarial-1","title":"A Self-supervised Approach for Adversarial Robustness","date":"2020-06-08","arxiv_id":"2006.04924","repositories_listed":2,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-self-supervised-approach-for-adversarial-1#ran","syntology_url":"https://syntology.ai/paper/2006.04924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04924"}},"official":{"repos":["Muzammal-Naseer/NRP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/black-box-explanation-of-object-detectors-via","slug":"black-box-explanation-of-object-detectors-via","title":"Black-box Explanation of Object Detectors via Saliency Maps","date":"2020-06-05","arxiv_id":"2006.03204","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/black-box-explanation-of-object-detectors-via#ran","syntology_url":"https://syntology.ai/paper/2006.03204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03204"}},"official":null}},{"url":"/paper/multi-interactive-encoder-decoder-network-for-1","slug":"multi-interactive-encoder-decoder-network-for-1","title":"Multi-interactive Encoder-decoder Network for RGBT Salient Object Detection","date":"2020-06-05","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/fbnetv3-joint-architecture-recipe-search","slug":"fbnetv3-joint-architecture-recipe-search","title":"FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining","date":"2020-06-03","arxiv_id":"2006.02049","repositories_listed":2,"syntology":null},{"url":"/paper/saliencymix-a-saliency-guided-data","slug":"saliencymix-a-saliency-guided-data","title":"SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization","date":"2020-06-02","arxiv_id":"2006.01791","repositories_listed":2,"syntology":null},{"url":"/paper/camouflaged-object-detection","slug":"camouflaged-object-detection","title":"Camouflaged Object Detection","date":"2020-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/distilling-image-dehazing-with-heterogeneous","slug":"distilling-image-dehazing-with-heterogeneous","title":"Distilling Image Dehazing With Heterogeneous Task Imitation","date":"2020-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/attention-guided-context-feature-pyramid","slug":"attention-guided-context-feature-pyramid","title":"Attention-guided Context Feature Pyramid Network for Object Detection","date":"2020-05-23","arxiv_id":"2005.11475","repositories_listed":2,"syntology":null},{"url":"/paper/scale-equalizing-pyramid-convolution-for","slug":"scale-equalizing-pyramid-convolution-for","title":"Scale-Equalizing Pyramid Convolution for Object Detection","date":"2020-05-06","arxiv_id":"2005.03101","repositories_listed":2,"syntology":null},{"url":"/paper/multi-interactive-encoder-decoder-network-for","slug":"multi-interactive-encoder-decoder-network-for","title":"Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection","date":"2020-05-05","arxiv_id":"2005.02315","repositories_listed":2,"syntology":null},{"url":"/paper/how-to-train-your-energy-based-model-for","slug":"how-to-train-your-energy-based-model-for","title":"How to Train Your Energy-Based Model for Regression","date":"2020-05-04","arxiv_id":"2005.01698","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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","sample_list":"/paper/how-to-train-your-energy-based-model-for#ran","syntology_url":"https://syntology.ai/paper/2005.01698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01698"}},"official":{"repos":["fregu856/ebms_regression"],"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"]}}},{"url":"/paper/monitoring-covid-19-social-distancing-with","slug":"monitoring-covid-19-social-distancing-with","title":"Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques","date":"2020-05-04","arxiv_id":"2005.01385","repositories_listed":2,"syntology":null},{"url":"/paper/image-captioning-through-image-transformer","slug":"image-captioning-through-image-transformer","title":"Image Captioning through Image Transformer","date":"2020-04-29","arxiv_id":"2004.14231","repositories_listed":2,"syntology":null},{"url":"/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","repositories_listed":2,"syntology":null},{"url":"/paper/measuring-human-and-economic-activity-from","slug":"measuring-human-and-economic-activity-from","title":"Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic","date":"2020-04-16","arxiv_id":"2004.07438","repositories_listed":2,"syntology":null},{"url":"/paper/improved-residual-networks-for-image-and","slug":"improved-residual-networks-for-image-and","title":"Improved Residual Networks for Image and Video Recognition","date":"2020-04-10","arxiv_id":"2004.04989","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improved-residual-networks-for-image-and#ran","syntology_url":"https://syntology.ai/paper/2004.04989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04989"}},"official":{"repos":["iduta/iresnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-aware-context-focused-and-memory","slug":"instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","date":"2020-04-09","arxiv_id":"2004.04725","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/instance-aware-context-focused-and-memory#ran","syntology_url":"https://syntology.ai/paper/2004.04725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04725"}},"official":{"repos":["NVlabs/wetectron"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/tog-targeted-adversarial-objectness-gradient","slug":"tog-targeted-adversarial-objectness-gradient","title":"TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems","date":"2020-04-09","arxiv_id":"2004.04320","repositories_listed":2,"syntology":null},{"url":"/paper/binary-neural-networks-a-survey","slug":"binary-neural-networks-a-survey","title":"Binary Neural Networks: A Survey","date":"2020-03-31","arxiv_id":"2004.03333","repositories_listed":2,"syntology":null},{"url":"/paper/look-into-object-self-supervised-structure","slug":"look-into-object-self-supervised-structure","title":"Look-into-Object: Self-supervised Structure Modeling for Object Recognition","date":"2020-03-31","arxiv_id":"2003.14142","repositories_listed":2,"syntology":null},{"url":"/paper/squeezed-deep-6dof-object-detection-using","slug":"squeezed-deep-6dof-object-detection-using","title":"Squeezed Deep 6DoF Object Detection Using Knowledge Distillation","date":"2020-03-30","arxiv_id":"2003.13586","repositories_listed":2,"syntology":null},{"url":"/paper/memory-enhanced-global-local-aggregation-for","slug":"memory-enhanced-global-local-aggregation-for","title":"Memory Enhanced Global-Local Aggregation for Video Object Detection","date":"2020-03-26","arxiv_id":"2003.12063","repositories_listed":2,"syntology":null},{"url":"/paper/centripetalnet-pursuing-high-quality-keypoint","slug":"centripetalnet-pursuing-high-quality-keypoint","title":"CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection","date":"2020-03-20","arxiv_id":"2003.09119","repositories_listed":2,"syntology":null},{"url":"/paper/1st-place-solutions-for-openimage2019-object","slug":"1st-place-solutions-for-openimage2019-object","title":"1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation","date":"2020-03-17","arxiv_id":"2003.07557","repositories_listed":2,"syntology":null},{"url":"/paper/incremental-object-detection-via-meta","slug":"incremental-object-detection-via-meta","title":"Incremental Object Detection via Meta-Learning","date":"2020-03-17","arxiv_id":"2003.08798","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/incremental-object-detection-via-meta#ran","syntology_url":"https://syntology.ai/paper/2003.08798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.08798"}},"official":{"repos":["JosephKJ/iOD"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/revisiting-the-sibling-head-in-object","slug":"revisiting-the-sibling-head-in-object","title":"Revisiting the Sibling Head in Object Detector","date":"2020-03-17","arxiv_id":"2003.07540","repositories_listed":2,"syntology":null},{"url":"/paper/motionnet-joint-perception-and-motion","slug":"motionnet-joint-perception-and-motion","title":"MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps","date":"2020-03-15","arxiv_id":"2003.06754","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/motionnet-joint-perception-and-motion#ran","syntology_url":"https://syntology.ai/paper/2003.06754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06754"}},"official":{"repos":["pxiangwu/MotionNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-hough-transform-for-semantic-line","slug":"deep-hough-transform-for-semantic-line","title":"Deep Hough Transform for Semantic Line Detection","date":"2020-03-10","arxiv_id":"2003.04676","repositories_listed":2,"syntology":null},{"url":"/paper/bidet-an-efficient-binarized-object-detector","slug":"bidet-an-efficient-binarized-object-detector","title":"BiDet: An Efficient Binarized Object Detector","date":"2020-03-09","arxiv_id":"2003.03961","repositories_listed":2,"syntology":null},{"url":"/paper/birdnet-end-to-end-3d-object-detection-in","slug":"birdnet-end-to-end-3d-object-detection-in","title":"BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View","date":"2020-03-09","arxiv_id":"2003.04188","repositories_listed":2,"syntology":null},{"url":"/paper/global-context-aware-progressive-aggregation","slug":"global-context-aware-progressive-aggregation","title":"Global Context-Aware Progressive Aggregation Network for Salient Object Detection","date":"2020-03-02","arxiv_id":"2003.00651","repositories_listed":2,"syntology":{"n":13,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":13,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/global-context-aware-progressive-aggregation#ran","syntology_url":"https://syntology.ai/paper/2003.00651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00651"}},"official":{"repos":["JosephChenHub/GCPANet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/plug-play-convolutional-regression-tracker","slug":"plug-play-convolutional-regression-tracker","title":"Plug & Play Convolutional Regression Tracker for Video Object Detection","date":"2020-03-02","arxiv_id":"2003.00981","repositories_listed":2,"syntology":null},{"url":"/paper/algorithm-hardware-co-design-for-deformable","slug":"algorithm-hardware-co-design-for-deformable","title":"Algorithm-hardware Co-design for Deformable Convolution","date":"2020-02-19","arxiv_id":"2002.08357","repositories_listed":2,"syntology":null},{"url":"/paper/cross-iteration-batch-normalization","slug":"cross-iteration-batch-normalization","title":"Cross-Iteration Batch Normalization","date":"2020-02-13","arxiv_id":"2002.05712","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/cross-iteration-batch-normalization#ran","syntology_url":"https://syntology.ai/paper/2002.05712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05712"}},"official":{"repos":["Howal/Cross-iterationBatchNorm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/solving-missing-annotation-object-detection","slug":"solving-missing-annotation-object-detection","title":"Solving Missing-Annotation Object Detection with Background Recalibration Loss","date":"2020-02-12","arxiv_id":"2002.05274","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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","sample_list":"/paper/solving-missing-annotation-object-detection#ran","syntology_url":"https://syntology.ai/paper/2002.05274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05274"}},"official":{"repos":["Dwrety/mmdetection-selective-iou"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/a-fixation-based-360-benchmark-dataset-for","slug":"a-fixation-based-360-benchmark-dataset-for","title":"A Fixation-based 360° Benchmark Dataset for Salient Object Detection","date":"2020-01-22","arxiv_id":"2001.07960","repositories_listed":2,"syntology":null},{"url":"/paper/codereef-an-open-platform-for-portable-mlops","slug":"codereef-an-open-platform-for-portable-mlops","title":"CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking","date":"2020-01-22","arxiv_id":"2001.07935","repositories_listed":2,"syntology":null},{"url":"/paper/vision-meets-drones-past-present-and-future","slug":"vision-meets-drones-past-present-and-future","title":"Detection and Tracking Meet Drones Challenge","date":"2020-01-16","arxiv_id":"2001.06303","repositories_listed":2,"syntology":null},{"url":"/paper/scale-match-for-tiny-person-detection","slug":"scale-match-for-tiny-person-detection","title":"Scale Match for Tiny Person Detection","date":"2019-12-23","arxiv_id":"1912.10664","repositories_listed":2,"syntology":null},{"url":"/paper/treynet-a-neural-model-for-text-localization","slug":"treynet-a-neural-model-for-text-localization","title":"A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages","date":"2019-12-20","arxiv_id":"1912.10016","repositories_listed":2,"syntology":null},{"url":"/paper/solving-visual-object-ambiguities-when","slug":"solving-visual-object-ambiguities-when","title":"Solving Visual Object Ambiguities when Pointing: An Unsupervised Learning Approach","date":"2019-12-13","arxiv_id":"1912.06449","repositories_listed":2,"syntology":null},{"url":"/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","repositories_listed":2,"syntology":null},{"url":"/paper/tanet-robust-3d-object-detection-from-point","slug":"tanet-robust-3d-object-detection-from-point","title":"TANet: Robust 3D Object Detection from Point Clouds with Triple Attention","date":"2019-12-11","arxiv_id":"1912.05163","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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","sample_list":"/paper/tanet-robust-3d-object-detection-from-point#ran","syntology_url":"https://syntology.ai/paper/1912.05163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05163"}},"official":null}},{"url":"/paper/learning-depth-guided-convolutions-for","slug":"learning-depth-guided-convolutions-for","title":"Learning Depth-Guided Convolutions for Monocular 3D Object Detection","date":"2019-12-10","arxiv_id":"1912.04799","repositories_listed":2,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 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; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/learning-depth-guided-convolutions-for#ran","syntology_url":"https://syntology.ai/paper/1912.04799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.04799"}},"official":{"repos":["dingmyu/D4LCN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/mnasfpn-learning-latency-aware-pyramid","slug":"mnasfpn-learning-latency-aware-pyramid","title":"MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices","date":"2019-12-02","arxiv_id":"1912.01106","repositories_listed":2,"syntology":null},{"url":"/paper/consistency-based-semi-supervised-learning","slug":"consistency-based-semi-supervised-learning","title":"Consistency-based Semi-supervised Learning for Object detection","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/mussp-efficient-min-cost-flow-algorithm-for","slug":"mussp-efficient-min-cost-flow-algorithm-for","title":"muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/one-shot-object-detection-with-co-attention-1","slug":"one-shot-object-detection-with-co-attention-1","title":"One-Shot Object Detection with Co-Attention and Co-Excitation","date":"2019-11-28","arxiv_id":"1911.12529","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/one-shot-object-detection-with-co-attention-1#ran","syntology_url":"https://syntology.ai/paper/1911.12529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12529"}},"official":{"repos":["timy90022/One-Shot-Object-Detection"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/soft-anchor-point-object-detection","slug":"soft-anchor-point-object-detection","title":"Soft Anchor-Point Object Detection","date":"2019-11-27","arxiv_id":"1911.12448","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 2 unverified","sample_list":"/paper/soft-anchor-point-object-detection#ran","syntology_url":"https://syntology.ai/paper/1911.12448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12448"}},"official":null}},{"url":"/paper/meta-learning-of-neural-architectures-for-few","slug":"meta-learning-of-neural-architectures-for-few","title":"Meta-Learning of Neural Architectures for Few-Shot Learning","date":"2019-11-25","arxiv_id":"1911.11090","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/meta-learning-of-neural-architectures-for-few#ran","syntology_url":"https://syntology.ai/paper/1911.11090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.11090"}},"official":{"repos":["boschresearch/metanas"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-cross-task-black-box","slug":"enhancing-cross-task-black-box","title":"Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction","date":"2019-11-22","arxiv_id":"1911.11616","repositories_listed":2,"syntology":null},{"url":"/paper/learning-modulated-loss-for-rotated-object","slug":"learning-modulated-loss-for-rotated-object","title":"Learning Modulated Loss for Rotated Object Detection","date":"2019-11-19","arxiv_id":"1911.08299","repositories_listed":2,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/learning-modulated-loss-for-rotated-object#ran","syntology_url":"https://syntology.ai/paper/1911.08299","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08299"}},"official":null}},{"url":"/paper/hawq-v2-hessian-aware-trace-weighted","slug":"hawq-v2-hessian-aware-trace-weighted","title":"HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks","date":"2019-11-10","arxiv_id":"1911.03852","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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) · 2 unverified","sample_list":"/paper/hawq-v2-hessian-aware-trace-weighted#ran","syntology_url":"https://syntology.ai/paper/1911.03852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03852"}},"official":null}},{"url":"/paper/making-an-invisibility-cloak-real-world","slug":"making-an-invisibility-cloak-real-world","title":"Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors","date":"2019-10-31","arxiv_id":"1910.14667","repositories_listed":2,"syntology":null},{"url":"/paper/a-maximum-likelihood-approach-to-extract","slug":"a-maximum-likelihood-approach-to-extract","title":"A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans","date":"2019-10-23","arxiv_id":"1910.11146","repositories_listed":2,"syntology":null},{"url":"/paper/real-world-image-datasets-for-federated","slug":"real-world-image-datasets-for-federated","title":"Real-World Image Datasets for Federated Learning","date":"2019-10-14","arxiv_id":"1910.11089","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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","sample_list":"/paper/real-world-image-datasets-for-federated#ran","syntology_url":"https://syntology.ai/paper/1910.11089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11089"}},"official":null}},{"url":"/paper/a-closer-look-at-network-resolution-for","slug":"a-closer-look-at-network-resolution-for","title":"MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution","date":"2019-09-27","arxiv_id":"1909.12978","repositories_listed":2,"syntology":null},{"url":"/paper/dctd-deep-conditional-target-densities-for","slug":"dctd-deep-conditional-target-densities-for","title":"Energy-Based Models for Deep Probabilistic Regression","date":"2019-09-26","arxiv_id":"1909.12297","repositories_listed":2,"syntology":null},{"url":"/paper/fast-and-accurate-convolutional-object","slug":"fast-and-accurate-convolutional-object","title":"Development of Fast Refinement Detectors on AI Edge Platforms","date":"2019-09-24","arxiv_id":"1909.10798","repositories_listed":2,"syntology":null},{"url":"/paper/190909709","slug":"190909709","title":"SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems","date":"2019-09-20","arxiv_id":"1909.09709","repositories_listed":2,"syntology":null},{"url":"/paper/global-aggregation-then-local-distribution-in","slug":"global-aggregation-then-local-distribution-in","title":"Global Aggregation then Local Distribution in Fully Convolutional Networks","date":"2019-09-16","arxiv_id":"1909.07229","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/global-aggregation-then-local-distribution-in#ran","syntology_url":"https://syntology.ai/paper/1909.07229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07229"}},"official":{"repos":["lxtGH/GALD-Net"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/motion-guided-attention-for-video-salient","slug":"motion-guided-attention-for-video-salient","title":"Motion Guided Attention for Video Salient Object Detection","date":"2019-09-16","arxiv_id":"1909.07061","repositories_listed":2,"syntology":null},{"url":"/paper/relation-distillation-networks-for-video","slug":"relation-distillation-networks-for-video","title":"Relation Distillation Networks for Video Object Detection","date":"2019-08-26","arxiv_id":"1908.09511","repositories_listed":2,"syntology":null}],"record_sha256":"8513584fcf9e69c4e25000e4e5bfc59b7ec4ceaa5e3726975ec15aa5700bf02d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}