{"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/27","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":27,"pages_in_order":106,"rows_per_page":100,"rows":[2601,2700],"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/26","next":"/task/object-detection-1/papers/28","papers":[{"url":"/paper/bridging-the-domain-gap-for-multi-agent","slug":"bridging-the-domain-gap-for-multi-agent","title":"Bridging the Domain Gap for Multi-Agent Perception","date":"2022-10-16","arxiv_id":"2210.08451","repositories_listed":1,"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/bridging-the-domain-gap-for-multi-agent#ran","syntology_url":"https://syntology.ai/paper/2210.08451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08451"}},"official":{"repos":["derrickxunu/mpda"],"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/move-unsupervised-movable-object-segmentation","slug":"move-unsupervised-movable-object-segmentation","title":"MOVE: Unsupervised Movable Object Segmentation and Detection","date":"2022-10-14","arxiv_id":"2210.07920","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":6,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/move-unsupervised-movable-object-segmentation#ran","syntology_url":"https://syntology.ai/paper/2210.07920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07920"}},"official":{"repos":["adambielski/move-seg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sailor-scaling-anchors-via-insights-into","slug":"sailor-scaling-anchors-via-insights-into","title":"SAILOR: Scaling Anchors via Insights into Latent Object Representation","date":"2022-10-14","arxiv_id":"2210.07811","repositories_listed":1,"syntology":null},{"url":"/paper/dimensionality-of-datasets-in-object","slug":"dimensionality-of-datasets-in-object","title":"Dimensionality of datasets in object detection networks","date":"2022-10-13","arxiv_id":"2210.07049","repositories_listed":1,"syntology":null},{"url":"/paper/imaginarynet-learning-object-detectors","slug":"imaginarynet-learning-object-detectors","title":"ImaginaryNet: Learning Object Detectors without Real Images and Annotations","date":"2022-10-13","arxiv_id":"2210.06886","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/imaginarynet-learning-object-detectors#ran","syntology_url":"https://syntology.ai/paper/2210.06886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06886"}},"official":{"repos":["kodenii/imaginarynet"],"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"]}}},{"url":"/paper/mffn-multi-view-feature-fusion-network-for","slug":"mffn-multi-view-feature-fusion-network-for","title":"MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection","date":"2022-10-12","arxiv_id":"2210.06361","repositories_listed":1,"syntology":null},{"url":"/paper/cd-fsod-a-benchmark-for-cross-domain-few-shot","slug":"cd-fsod-a-benchmark-for-cross-domain-few-shot","title":"CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection","date":"2022-10-11","arxiv_id":"2210.05311","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fourier-up-sampling","slug":"deep-fourier-up-sampling","title":"Deep Fourier Up-Sampling","date":"2022-10-11","arxiv_id":"2210.05171","repositories_listed":1,"syntology":null},{"url":"/paper/ensemblemot-a-step-towards-ensemble-learning","slug":"ensemblemot-a-step-towards-ensemble-learning","title":"EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking","date":"2022-10-11","arxiv_id":"2210.05278","repositories_listed":1,"syntology":null},{"url":"/paper/improving-long-tailed-object-detection-with","slug":"improving-long-tailed-object-detection-with","title":"Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning","date":"2022-10-11","arxiv_id":"2210.05568","repositories_listed":1,"syntology":null},{"url":"/paper/opera-omni-supervised-representation-learning","slug":"opera-omni-supervised-representation-learning","title":"OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions","date":"2022-10-11","arxiv_id":"2210.05557","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/opera-omni-supervised-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2210.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05557"}},"official":{"repos":["wangck20/opera"],"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","unlocated"]}}},{"url":"/paper/prototypical-votenet-for-few-shot-3d-point","slug":"prototypical-votenet-for-few-shot-3d-point","title":"Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection","date":"2022-10-11","arxiv_id":"2210.05593","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/prototypical-votenet-for-few-shot-3d-point#ran","syntology_url":"https://syntology.ai/paper/2210.05593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05593"}},"official":{"repos":["cvmi-lab/fs3d"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/the-equalization-losses-gradient-driven","slug":"the-equalization-losses-gradient-driven","title":"The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition","date":"2022-10-11","arxiv_id":"2210.05566","repositories_listed":1,"syntology":null},{"url":"/paper/4d-unsupervised-object-discovery","slug":"4d-unsupervised-object-discovery","title":"4D Unsupervised Object Discovery","date":"2022-10-10","arxiv_id":"2210.04801","repositories_listed":1,"syntology":null},{"url":"/paper/cagroup3d-class-aware-grouping-for-3d-object","slug":"cagroup3d-class-aware-grouping-for-3d-object","title":"CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds","date":"2022-10-09","arxiv_id":"2210.04264","repositories_listed":1,"syntology":null},{"url":"/paper/volta-vision-language-transformer-with-weakly","slug":"volta-vision-language-transformer-with-weakly","title":"VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment","date":"2022-10-09","arxiv_id":"2210.04135","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/volta-vision-language-transformer-with-weakly#ran","syntology_url":"https://syntology.ai/paper/2210.04135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04135"}},"official":{"repos":["ShramanPramanick/VoLTA"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/hierarchical-few-shot-object-detection","slug":"hierarchical-few-shot-object-detection","title":"Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method","date":"2022-10-08","arxiv_id":"2210.03940","repositories_listed":1,"syntology":null},{"url":"/paper/training-deep-learning-algorithms-on","slug":"training-deep-learning-algorithms-on","title":"Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection","date":"2022-10-08","arxiv_id":"2210.04104","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-method-for-detecting-asphalt","slug":"an-efficient-method-for-detecting-asphalt","title":"An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model","date":"2022-10-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-investigation-into-whitening-loss-for-self","slug":"an-investigation-into-whitening-loss-for-self","title":"An Investigation into Whitening Loss for Self-supervised Learning","date":"2022-10-07","arxiv_id":"2210.03586","repositories_listed":1,"syntology":null},{"url":"/paper/clad-a-realistic-continual-learning-benchmark","slug":"clad-a-realistic-continual-learning-benchmark","title":"CLAD: A realistic Continual Learning benchmark for Autonomous Driving","date":"2022-10-07","arxiv_id":"2210.03482","repositories_listed":1,"syntology":null},{"url":"/paper/humans-need-not-label-more-humans-occlusion","slug":"humans-need-not-label-more-humans-occlusion","title":"Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation","date":"2022-10-07","arxiv_id":"2210.03686","repositories_listed":1,"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/humans-need-not-label-more-humans-occlusion#ran","syntology_url":"https://syntology.ai/paper/2210.03686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03686"}},"official":{"repos":["levan92/occlusion-copy-paste"],"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/ida-det-an-information-discrepancy-aware","slug":"ida-det-an-information-discrepancy-aware","title":"IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors","date":"2022-10-07","arxiv_id":"2210.03477","repositories_listed":1,"syntology":null},{"url":"/paper/simulating-single-photon-detector-array","slug":"simulating-single-photon-detector-array","title":"Simulating single-photon detector array sensors for depth imaging","date":"2022-10-07","arxiv_id":"2210.05644","repositories_listed":1,"syntology":null},{"url":"/paper/a-review-of-uncertainty-calibration-in","slug":"a-review-of-uncertainty-calibration-in","title":"A Review of Uncertainty Calibration in Pretrained Object Detectors","date":"2022-10-06","arxiv_id":"2210.02935","repositories_listed":1,"syntology":null},{"url":"/paper/effective-self-supervised-pre-training-on-low","slug":"effective-self-supervised-pre-training-on-low","title":"Effective Self-supervised Pre-training on Low-compute Networks without Distillation","date":"2022-10-06","arxiv_id":"2210.02808","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/effective-self-supervised-pre-training-on-low#ran","syntology_url":"https://syntology.ai/paper/2210.02808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02808"}},"official":{"repos":["saic-fi/sslight"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/centralized-feature-pyramid-for-object","slug":"centralized-feature-pyramid-for-object","title":"Centralized Feature Pyramid for Object Detection","date":"2022-10-05","arxiv_id":"2210.02093","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/centralized-feature-pyramid-for-object#ran","syntology_url":"https://syntology.ai/paper/2210.02093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02093"}},"official":{"repos":["qy1994-0919/cfpnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-effective-knowledge-transfer-for","slug":"exploring-effective-knowledge-transfer-for","title":"Exploring Effective Knowledge Transfer for Few-shot Object Detection","date":"2022-10-05","arxiv_id":"2210.02021","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-role-of-mean-teachers-in-self","slug":"exploring-the-role-of-mean-teachers-in-self","title":"Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders","date":"2022-10-05","arxiv_id":"2210.02077","repositories_listed":1,"syntology":null},{"url":"/paper/time-will-tell-new-outlooks-and-a-baseline","slug":"time-will-tell-new-outlooks-and-a-baseline","title":"Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection","date":"2022-10-05","arxiv_id":"2210.02443","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/time-will-tell-new-outlooks-and-a-baseline#ran","syntology_url":"https://syntology.ai/paper/2210.02443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02443"}},"official":{"repos":["divadi/solofusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/wuda-unsupervised-domain-adaptation-based-on","slug":"wuda-unsupervised-domain-adaptation-based-on","title":"WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels","date":"2022-10-05","arxiv_id":"2210.02088","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-camera-unsupervised-domain-adaptation-1","slug":"a-multi-camera-unsupervised-domain-adaptation-1","title":"A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training","date":"2022-10-03","arxiv_id":"2210.00808","repositories_listed":1,"syntology":null},{"url":"/paper/detfusion-a-detection-driven-infrared-and","slug":"detfusion-a-detection-driven-infrared-and","title":"DetFusion: A Detection-driven Infrared and Visible Image Fusion Network","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pseudo-label-generation-and-various-data","slug":"pseudo-label-generation-and-various-data","title":"Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-in-depth-study-of-stochastic","slug":"an-in-depth-study-of-stochastic","title":"An In-depth Study of Stochastic Backpropagation","date":"2022-09-30","arxiv_id":"2210.00129","repositories_listed":1,"syntology":null},{"url":"/paper/bayesft-bayesian-optimization-for-fault","slug":"bayesft-bayesian-optimization-for-fault","title":"BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture","date":"2022-09-30","arxiv_id":"2210.01795","repositories_listed":1,"syntology":null},{"url":"/paper/d-align-dual-query-co-attention-network-for","slug":"d-align-dual-query-co-attention-network-for","title":"D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence","date":"2022-09-30","arxiv_id":"2210.00087","repositories_listed":1,"syntology":null},{"url":"/paper/f-vlm-open-vocabulary-object-detection-upon","slug":"f-vlm-open-vocabulary-object-detection-upon","title":"F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models","date":"2022-09-30","arxiv_id":"2209.15639","repositories_listed":1,"syntology":null},{"url":"/paper/pointpillars-backbone-type-selection-for-fast","slug":"pointpillars-backbone-type-selection-for-fast","title":"PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection","date":"2022-09-30","arxiv_id":"2209.15252","repositories_listed":1,"syntology":null},{"url":"/paper/gdip-gated-differentiable-image-processing","slug":"gdip-gated-differentiable-image-processing","title":"GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions","date":"2022-09-29","arxiv_id":"2209.14922","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-dataset-for-evaluating-and","slug":"a-novel-dataset-for-evaluating-and","title":"A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields","date":"2022-09-27","arxiv_id":"2209.13202","repositories_listed":1,"syntology":null},{"url":"/paper/crossdtr-cross-view-and-depth-guided","slug":"crossdtr-cross-view-and-depth-guided","title":"CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection","date":"2022-09-27","arxiv_id":"2209.13507","repositories_listed":1,"syntology":null},{"url":"/paper/obbstacking-an-ensemble-method-for-remote","slug":"obbstacking-an-ensemble-method-for-remote","title":"OBBStacking: An Ensemble Method for Remote Sensing Object Detection","date":"2022-09-27","arxiv_id":"2209.13369","repositories_listed":1,"syntology":null},{"url":"/paper/observation-centric-and-central-distance","slug":"observation-centric-and-central-distance","title":"Observation Centric and Central Distance Recovery on Sports Player Tracking","date":"2022-09-27","arxiv_id":"2209.13154","repositories_listed":1,"syntology":null},{"url":"/paper/where2comm-communication-efficient","slug":"where2comm-communication-efficient","title":"Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps","date":"2022-09-26","arxiv_id":"2209.12836","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":1,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 1 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/where2comm-communication-efficient#ran","syntology_url":"https://syntology.ai/paper/2209.12836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.12836"}},"official":{"repos":["mediabrain-sjtu/where2comm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/localizing-anatomical-landmarks-in-ocular","slug":"localizing-anatomical-landmarks-in-ocular","title":"Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks","date":"2022-09-25","arxiv_id":"2210.02445","repositories_listed":1,"syntology":null},{"url":"/paper/acrofod-an-adaptive-method-for-cross-domain","slug":"acrofod-an-adaptive-method-for-cross-domain","title":"AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection","date":"2022-09-22","arxiv_id":"2209.10904","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/acrofod-an-adaptive-method-for-cross-domain#ran","syntology_url":"https://syntology.ai/paper/2209.10904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10904"}},"official":{"repos":["hlings/acrofod"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-data-augmentation-in-knowledge","slug":"rethinking-data-augmentation-in-knowledge","title":"Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation","date":"2022-09-20","arxiv_id":"2209.09841","repositories_listed":1,"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":4,"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/rethinking-data-augmentation-in-knowledge#ran","syntology_url":"https://syntology.ai/paper/2209.09841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.09841"}},"official":{"repos":["jwliang007/ikd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-dual-cycled-cross-view-transformer-network","slug":"a-dual-cycled-cross-view-transformer-network","title":"A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View","date":"2022-09-19","arxiv_id":"2209.08844","repositories_listed":1,"syntology":null},{"url":"/paper/glare-a-dataset-for-traffic-sign-detection-in","slug":"glare-a-dataset-for-traffic-sign-detection-in","title":"GLARE: A Dataset for Traffic Sign Detection in Sun Glare","date":"2022-09-19","arxiv_id":"2209.08716","repositories_listed":1,"syntology":null},{"url":"/paper/hapi-a-large-scale-longitudinal-dataset-of","slug":"hapi-a-large-scale-longitudinal-dataset-of","title":"HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions","date":"2022-09-18","arxiv_id":"2209.08443","repositories_listed":1,"syntology":null},{"url":"/paper/rdd2022-a-multi-national-image-dataset-for","slug":"rdd2022-a-multi-national-image-dataset-for","title":"RDD2022: A multi-national image dataset for automatic Road Damage Detection","date":"2022-09-18","arxiv_id":"2209.08538","repositories_listed":1,"syntology":null},{"url":"/paper/softgroup-scalable-3d-instance-segmentation","slug":"softgroup-scalable-3d-instance-segmentation","title":"Scalable SoftGroup for 3D Instance Segmentation on Point Clouds","date":"2022-09-17","arxiv_id":"2209.08263","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-of-collaborative","slug":"uncertainty-quantification-of-collaborative","title":"Uncertainty Quantification of Collaborative Detection for Self-Driving","date":"2022-09-16","arxiv_id":"2209.08162","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/uncertainty-quantification-of-collaborative#ran","syntology_url":"https://syntology.ai/paper/2209.08162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.08162"}},"official":null}},{"url":"/paper/viewer-centred-surface-completion-for","slug":"viewer-centred-surface-completion-for","title":"Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection","date":"2022-09-14","arxiv_id":"2209.06407","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/viewer-centred-surface-completion-for#ran","syntology_url":"https://syntology.ai/paper/2209.06407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06407"}},"official":{"repos":["darrenjkt/SEE-VCN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/psaq-vit-v2-towards-accurate-and-general-data","slug":"psaq-vit-v2-towards-accurate-and-general-data","title":"PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers","date":"2022-09-13","arxiv_id":"2209.05687","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"5 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/psaq-vit-v2-towards-accurate-and-general-data#ran","syntology_url":"https://syntology.ai/paper/2209.05687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05687"}},"official":{"repos":["zkkli/psaq-vit"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/centerformer-center-based-transformer-for-3d","slug":"centerformer-center-based-transformer-for-3d","title":"CenterFormer: Center-based Transformer for 3D Object Detection","date":"2022-09-12","arxiv_id":"2209.05588","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/centerformer-center-based-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2209.05588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05588"}},"official":{"repos":["tusimple/centerformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-sparsification-of-graph-neural","slug":"towards-sparsification-of-graph-neural","title":"Towards Sparsification of Graph Neural Networks","date":"2022-09-11","arxiv_id":"2209.04766","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/towards-sparsification-of-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2209.04766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.04766"}},"official":{"repos":["harveyp123/iccd_sptrn_slr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ir-lpr-large-scale-of-iranian-license-plate","slug":"ir-lpr-large-scale-of-iranian-license-plate","title":"IR-LPR: Large Scale of Iranian License Plate Recognition Dataset","date":"2022-09-10","arxiv_id":"2209.04680","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-target-representations-for-masked","slug":"exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","arxiv_id":"2209.03917","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploring-target-representations-for-masked#ran","syntology_url":"https://syntology.ai/paper/2209.03917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03917"}},"official":{"repos":["liuxingbin/dbot"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hardware-faults-that-matter-understanding-and","slug":"hardware-faults-that-matter-understanding-and","title":"Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs","date":"2022-09-07","arxiv_id":"2209.03225","repositories_listed":1,"syntology":null},{"url":"/paper/msmdfusion-fusing-lidar-and-camera-at","slug":"msmdfusion-fusing-lidar-and-camera-at","title":"MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection","date":"2022-09-07","arxiv_id":"2209.03102","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-angle-representation-for-remote","slug":"multi-grained-angle-representation-for-remote","title":"Multi-Grained Angle Representation for Remote Sensing Object Detection","date":"2022-09-07","arxiv_id":"2209.02884","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/ptseformer-progressive-temporal-spatial","slug":"ptseformer-progressive-temporal-spatial","title":"PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection","date":"2022-09-06","arxiv_id":"2209.02242","repositories_listed":1,"syntology":{"n":21,"n_ran":14,"n_constructed":11,"n_ran_checked":13,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"14 ran (of which 11 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/ptseformer-progressive-temporal-spatial#ran","syntology_url":"https://syntology.ai/paper/2209.02242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.02242"}},"official":{"repos":["hon-wong/ptseformer"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":11,"n_ran_no_instrument_failure":13,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/task-wise-sampling-convolutions-for-arbitrary","slug":"task-wise-sampling-convolutions-for-arbitrary","title":"Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images","date":"2022-09-06","arxiv_id":"2209.02200","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-detection-attacking-object","slug":"adversarial-detection-attacking-object","title":"Adversarial Detection: Attacking Object Detection in Real Time","date":"2022-09-05","arxiv_id":"2209.01962","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-teacher-provides-better-1","slug":"consistent-teacher-provides-better-1","title":"Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection","date":"2022-09-04","arxiv_id":"2209.01589","repositories_listed":1,"syntology":null},{"url":"/paper/togethernet-bridging-image-restoration-and","slug":"togethernet-bridging-image-restoration-and","title":"TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning","date":"2022-09-03","arxiv_id":"2209.01373","repositories_listed":1,"syntology":null},{"url":"/paper/visual-prompting-via-image-inpainting","slug":"visual-prompting-via-image-inpainting","title":"Visual Prompting via Image Inpainting","date":"2022-09-01","arxiv_id":"2209.00647","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-pyramid-representation","slug":"self-supervised-pyramid-representation","title":"Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond","date":"2022-08-30","arxiv_id":"2208.14439","repositories_listed":1,"syntology":null},{"url":"/paper/noisy-inliers-make-great-outliers-out-of","slug":"noisy-inliers-make-great-outliers-out-of","title":"SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection","date":"2022-08-29","arxiv_id":"2208.13930","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/noisy-inliers-make-great-outliers-out-of#ran","syntology_url":"https://syntology.ai/paper/2208.13930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13930"}},"official":{"repos":["samwilso/safe_official"],"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"]}}},{"url":"/paper/radial-prediction-domain-adaption-classifier","slug":"radial-prediction-domain-adaption-classifier","title":"Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge","date":"2022-08-29","arxiv_id":"2208.13902","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/disentangle-and-remerge-interventional","slug":"disentangle-and-remerge-interventional","title":"Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective","date":"2022-08-26","arxiv_id":"2208.12681","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/disentangle-and-remerge-interventional#ran","syntology_url":"https://syntology.ai/paper/2208.12681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12681"}},"official":{"repos":["zyn-1101/dandr"],"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"]}}},{"url":"/paper/vevid-vision-enhancement-via-virtual","slug":"vevid-vision-enhancement-via-virtual","title":"VEViD: Vision Enhancement via Virtual diffraction and coherent Detection","date":"2022-08-25","arxiv_id":"2208.12366","repositories_listed":1,"syntology":null},{"url":"/paper/motion-robust-high-speed-light-weighted","slug":"motion-robust-high-speed-light-weighted","title":"Motion Robust High-Speed Light-Weighted Object Detection With Event Camera","date":"2022-08-24","arxiv_id":"2208.11602","repositories_listed":1,"syntology":null},{"url":"/paper/citysim-a-drone-based-vehicle-trajectory","slug":"citysim-a-drone-based-vehicle-trajectory","title":"CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins","date":"2022-08-23","arxiv_id":"2208.11036","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-baseline-for-multi-camera-3d-object","slug":"a-simple-baseline-for-multi-camera-3d-object","title":"A Simple Baseline for Multi-Camera 3D Object Detection","date":"2022-08-22","arxiv_id":"2208.10035","repositories_listed":1,"syntology":null},{"url":"/paper/yolov-making-still-image-object-detectors","slug":"yolov-making-still-image-object-detectors","title":"YOLOV: Making Still Image Object Detectors Great at Video Object Detection","date":"2022-08-20","arxiv_id":"2208.09686","repositories_listed":1,"syntology":null},{"url":"/paper/monopcns-monocular-3d-object-detection-via","slug":"monopcns-monocular-3d-object-detection-via","title":"MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection","date":"2022-08-19","arxiv_id":"2208.09446","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-environmental-violations-with","slug":"detecting-environmental-violations-with","title":"Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act","date":"2022-08-18","arxiv_id":"2208.08919","repositories_listed":1,"syntology":null},{"url":"/paper/gravos-gradient-based-voxel-selection-for-3d","slug":"gravos-gradient-based-voxel-selection-for-3d","title":"GraVoS: Voxel Selection for 3D Point-Cloud Detection","date":"2022-08-18","arxiv_id":"2208.08780","repositories_listed":1,"syntology":null},{"url":"/paper/gsrformer-grounded-situation-recognition","slug":"gsrformer-grounded-situation-recognition","title":"GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement","date":"2022-08-18","arxiv_id":"2208.08965","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gsrformer-grounded-situation-recognition#ran","syntology_url":"https://syntology.ai/paper/2208.08965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08965"}},"official":{"repos":["zhiqic/gsrformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rfla-gaussian-receptive-field-based-label","slug":"rfla-gaussian-receptive-field-based-label","title":"RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection","date":"2022-08-18","arxiv_id":"2208.08738","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-up-sampling-for-asynchronous-events","slug":"temporal-up-sampling-for-asynchronous-events","title":"Temporal Up-Sampling for Asynchronous Events","date":"2022-08-18","arxiv_id":"2208.08721","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-visual-perception-by-dispersible","slug":"unifying-visual-perception-by-dispersible","title":"Unifying Visual Perception by Dispersible Points Learning","date":"2022-08-18","arxiv_id":"2208.08630","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/unifying-visual-perception-by-dispersible#ran","syntology_url":"https://syntology.ai/paper/2208.08630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08630"}},"official":{"repos":["sense-x/unihead"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/restructurable-activation-networks","slug":"restructurable-activation-networks","title":"Restructurable Activation Networks","date":"2022-08-17","arxiv_id":"2208.08562","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-attention-network-for-few-shot","slug":"hierarchical-attention-network-for-few-shot","title":"Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning","date":"2022-08-15","arxiv_id":"2208.07039","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-for-object-detection","slug":"contrastive-learning-for-object-detection","title":"Contrastive Learning for Object Detection","date":"2022-08-12","arxiv_id":"2208.06412","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-for-ood-in-object","slug":"contrastive-learning-for-ood-in-object","title":"Contrastive Learning for OOD in Object detection","date":"2022-08-12","arxiv_id":"2208.06083","repositories_listed":1,"syntology":null},{"url":"/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","repositories_listed":1,"syntology":null},{"url":"/paper/mixskd-self-knowledge-distillation-from-mixup","slug":"mixskd-self-knowledge-distillation-from-mixup","title":"MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition","date":"2022-08-11","arxiv_id":"2208.05768","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 3 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) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mixskd-self-knowledge-distillation-from-mixup#ran","syntology_url":"https://syntology.ai/paper/2208.05768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05768"}},"official":{"repos":["winycg/self-kd-lib"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/object-detection-with-deep-reinforcement","slug":"object-detection-with-deep-reinforcement","title":"Object Detection with Deep Reinforcement Learning","date":"2022-08-09","arxiv_id":"2208.04511","repositories_listed":1,"syntology":null},{"url":"/paper/aerial-monocular-3d-object-detection","slug":"aerial-monocular-3d-object-detection","title":"Aerial Monocular 3D Object Detection","date":"2022-08-08","arxiv_id":"2208.03974","repositories_listed":1,"syntology":null},{"url":"/paper/depth-quality-inspired-feature-manipulation-1","slug":"depth-quality-inspired-feature-manipulation-1","title":"Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection","date":"2022-08-08","arxiv_id":"2208.03918","repositories_listed":1,"syntology":null},{"url":"/paper/graph-r-cnn-towards-accurate-3d-object","slug":"graph-r-cnn-towards-accurate-3d-object","title":"Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph","date":"2022-08-07","arxiv_id":"2208.03624","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-resolution-and-degradation-clues-as","slug":"exploring-resolution-and-degradation-clues-as","title":"Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection","date":"2022-08-05","arxiv_id":"2208.03062","repositories_listed":1,"syntology":null},{"url":"/paper/memory-guided-collaborative-attention-for","slug":"memory-guided-collaborative-attention-for","title":"Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization","date":"2022-08-05","arxiv_id":"2208.02960","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-semantically-aligned-vision","slug":"fine-grained-semantically-aligned-vision","title":"Fine-Grained Semantically Aligned Vision-Language Pre-Training","date":"2022-08-04","arxiv_id":"2208.02515","repositories_listed":1,"syntology":null}],"record_sha256":"2fe1b20c4843aeaad9318b18ed01796b4d4d01e6f1c6be032764f6b43c4151d4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}