{"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/33","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":33,"pages_in_order":106,"rows_per_page":100,"rows":[3201,3300],"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/32","next":"/task/object-detection-1/papers/34","papers":[{"url":"/paper/contrastive-learning-with-temporal-correlated","slug":"contrastive-learning-with-temporal-correlated","title":"Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays","date":"2021-09-07","arxiv_id":"2109.03233","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-distillation-using-hierarchical","slug":"knowledge-distillation-using-hierarchical","title":"Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution","date":"2021-09-07","arxiv_id":"2109.03075","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-events-and-frames","slug":"bridging-the-gap-between-events-and-frames","title":"Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation","date":"2021-09-06","arxiv_id":"2109.02618","repositories_listed":1,"syntology":null},{"url":"/paper/pyramid-r-cnn-towards-better-performance-and","slug":"pyramid-r-cnn-towards-better-performance-and","title":"Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection","date":"2021-09-06","arxiv_id":"2109.02499","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 3 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/pyramid-r-cnn-towards-better-performance-and#ran","syntology_url":"https://syntology.ai/paper/2109.02499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02499"}},"official":null}},{"url":"/paper/voxel-transformer-for-3d-object-detection","slug":"voxel-transformer-for-3d-object-detection","title":"Voxel Transformer for 3D Object Detection","date":"2021-09-06","arxiv_id":"2109.02497","repositories_listed":1,"syntology":{"n":7,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"0 ran · 7 unverified","sample_list":"/paper/voxel-transformer-for-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2109.02497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02497"}},"official":null}},{"url":"/paper/identification-of-driver-phone-usage","slug":"identification-of-driver-phone-usage","title":"Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking","date":"2021-09-05","arxiv_id":"2109.02119","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-approach-for-uav-small-object","slug":"a-comprehensive-approach-for-uav-small-object","title":"A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion","date":"2021-09-04","arxiv_id":"2109.01800","repositories_listed":1,"syntology":null},{"url":"/paper/moving-object-detection-for-event-based","slug":"moving-object-detection-for-event-based","title":"Moving Object Detection for Event-based Vision using k-means Clustering","date":"2021-09-04","arxiv_id":"2109.01879","repositories_listed":1,"syntology":null},{"url":"/paper/4d-net-for-learned-multi-modal-alignment","slug":"4d-net-for-learned-multi-modal-alignment","title":"4D-Net for Learned Multi-Modal Alignment","date":"2021-09-02","arxiv_id":"2109.01066","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/4d-net-for-learned-multi-modal-alignment#ran","syntology_url":"https://syntology.ai/paper/2109.01066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.01066"}},"official":null}},{"url":"/paper/spatio-temporal-self-supervised","slug":"spatio-temporal-self-supervised","title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","date":"2021-09-01","arxiv_id":"2109.00179","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/spatio-temporal-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2109.00179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.00179"}},"official":{"repos":["yichen928/STRL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/discriminative-semantic-feature-pyramid","slug":"discriminative-semantic-feature-pyramid","title":"Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection","date":"2021-08-31","arxiv_id":"2108.13775","repositories_listed":1,"syntology":null},{"url":"/paper/luai-challenge-2021-on-learning-to-understand","slug":"luai-challenge-2021-on-learning-to-understand","title":"LUAI Challenge 2021 on Learning to Understand Aerial Images","date":"2021-08-30","arxiv_id":"2108.13246","repositories_listed":1,"syntology":null},{"url":"/paper/decentralized-autofocusing-system-with","slug":"decentralized-autofocusing-system-with","title":"DASHA: Decentralized Autofocusing System with Hierarchical Agents","date":"2021-08-29","arxiv_id":"2108.12842","repositories_listed":1,"syntology":null},{"url":"/paper/fovea-foveated-image-magnification-for","slug":"fovea-foveated-image-magnification-for","title":"FOVEA: Foveated Image Magnification for Autonomous Navigation","date":"2021-08-27","arxiv_id":"2108.12102","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/fovea-foveated-image-magnification-for#ran","syntology_url":"https://syntology.ai/paper/2108.12102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.12102"}},"official":{"repos":["tchittesh/fovea"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-the-aligned-and-misaligned","slug":"rethinking-the-aligned-and-misaligned","title":"Rethinking the Misalignment Problem in Dense Object Detection","date":"2021-08-27","arxiv_id":"2108.12176","repositories_listed":1,"syntology":null},{"url":"/paper/chessmix-spatial-context-data-augmentation","slug":"chessmix-spatial-context-data-augmentation","title":"ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation","date":"2021-08-26","arxiv_id":"2108.11535","repositories_listed":1,"syntology":null},{"url":"/paper/autoshape-real-time-shape-aware-monocular-3d","slug":"autoshape-real-time-shape-aware-monocular-3d","title":"AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection","date":"2021-08-25","arxiv_id":"2108.11127","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/autoshape-real-time-shape-aware-monocular-3d#ran","syntology_url":"https://syntology.ai/paper/2108.11127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.11127"}},"official":{"repos":["zongdai/autoshape"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/wanderlust-online-continual-object-detection","slug":"wanderlust-online-continual-object-detection","title":"Wanderlust: Online Continual Object Detection in the Real World","date":"2021-08-25","arxiv_id":"2108.11005","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":1,"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/wanderlust-online-continual-object-detection#ran","syntology_url":"https://syntology.ai/paper/2108.11005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.11005"}},"official":null}},{"url":"/paper/improving-3d-object-detection-with-channel","slug":"improving-3d-object-detection-with-channel","title":"Improving 3D Object Detection with Channel-wise Transformer","date":"2021-08-23","arxiv_id":"2108.10723","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/improving-3d-object-detection-with-channel#ran","syntology_url":"https://syntology.ai/paper/2108.10723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.10723"}},"official":{"repos":["hlsheng1/ct3d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/odam-object-detection-association-and-mapping","slug":"odam-object-detection-association-and-mapping","title":"ODAM: Object Detection, Association, and Mapping using Posed RGB Video","date":"2021-08-23","arxiv_id":"2108.10165","repositories_listed":1,"syntology":null},{"url":"/paper/motsynth-how-can-synthetic-data-help","slug":"motsynth-how-can-synthetic-data-help","title":"MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?","date":"2021-08-21","arxiv_id":"2108.09518","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-edge-based-u-shape-network-for","slug":"multi-scale-edge-based-u-shape-network-for","title":"Multi-scale Edge-based U-shape Network for Salient Object Detection","date":"2021-08-21","arxiv_id":"2108.09408","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-data-aggregation-and","slug":"exploring-data-aggregation-and","title":"Exploring Data Aggregation and Transformations to Generalize across Visual Domains","date":"2021-08-20","arxiv_id":"2108.09208","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-based-automatic-defect","slug":"a-deep-learning-based-automatic-defect","title":"A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations","date":"2021-08-19","arxiv_id":"2108.08882","repositories_listed":1,"syntology":null},{"url":"/paper/inter-species-cell-detection-datasets-on","slug":"inter-species-cell-detection-datasets-on","title":"Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens","date":"2021-08-19","arxiv_id":"2108.08529","repositories_listed":1,"syntology":null},{"url":"/paper/gastric-cancer-detection-from-x-ray-images","slug":"gastric-cancer-detection-from-x-ray-images","title":"Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box Training","date":"2021-08-18","arxiv_id":"2108.08158","repositories_listed":1,"syntology":null},{"url":"/paper/plad-a-dataset-for-multi-size-power-line","slug":"plad-a-dataset-for-multi-size-power-line","title":"STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images","date":"2021-08-18","arxiv_id":"2108.07944","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-salient-object-detection-with","slug":"boosting-salient-object-detection-with","title":"Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net","date":"2021-08-17","arxiv_id":"2108.07851","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-convolutional-neural-networks","slug":"contextual-convolutional-neural-networks","title":"Contextual Convolutional Neural Networks","date":"2021-08-17","arxiv_id":"2108.07387","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-classification-equilibrium-in-long","slug":"exploring-classification-equilibrium-in-long","title":"Exploring Classification Equilibrium in Long-Tailed Object Detection","date":"2021-08-17","arxiv_id":"2108.07507","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-classification-equilibrium-in-long#ran","syntology_url":"https://syntology.ai/paper/2108.07507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07507"}},"official":{"repos":["fcjian/loce"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adacon-adaptive-context-aware-object","slug":"adacon-adaptive-context-aware-object","title":"AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices","date":"2021-08-16","arxiv_id":"2108.06850","repositories_listed":1,"syntology":null},{"url":"/paper/pit-position-invariant-transform-for-cross","slug":"pit-position-invariant-transform-for-cross","title":"PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation","date":"2021-08-16","arxiv_id":"2108.07142","repositories_listed":1,"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/pit-position-invariant-transform-for-cross#ran","syntology_url":"https://syntology.ai/paper/2108.07142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07142"}},"official":{"repos":["sheepooo/pit-position-invariant-transform"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pnp-3d-a-plug-and-play-for-3d-point-clouds","slug":"pnp-3d-a-plug-and-play-for-3d-point-clouds","title":"PnP-3D: A Plug-and-Play for 3D Point Clouds","date":"2021-08-16","arxiv_id":"2108.07378","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unconstrained-joint-hand-object","slug":"towards-unconstrained-joint-hand-object","title":"Towards unconstrained joint hand-object reconstruction from RGB videos","date":"2021-08-16","arxiv_id":"2108.07044","repositories_listed":1,"syntology":null},{"url":"/paper/vector-decomposed-disentanglement-for-domain","slug":"vector-decomposed-disentanglement-for-domain","title":"Vector-Decomposed Disentanglement for Domain-Invariant Object Detection","date":"2021-08-15","arxiv_id":"2108.06685","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-graph-with-meta-concepts-for","slug":"cross-modal-graph-with-meta-concepts-for","title":"Cross-Modal Graph with Meta Concepts for Video Captioning","date":"2021-08-14","arxiv_id":"2108.06458","repositories_listed":1,"syntology":null},{"url":"/paper/eeea-net-an-early-exit-evolutionary-neural","slug":"eeea-net-an-early-exit-evolutionary-neural","title":"EEEA-Net: An Early Exit Evolutionary Neural Architecture Search","date":"2021-08-13","arxiv_id":"2108.06156","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-robustness-of-semantic","slug":"evaluating-the-robustness-of-semantic","title":"Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks","date":"2021-08-13","arxiv_id":"2108.06179","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-coordinate-transforms-for","slug":"progressive-coordinate-transforms-for","title":"Progressive Coordinate Transforms for Monocular 3D Object Detection","date":"2021-08-12","arxiv_id":"2108.05793","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/progressive-coordinate-transforms-for#ran","syntology_url":"https://syntology.ai/paper/2108.05793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05793"}},"official":{"repos":["amazon-research/progressive-coordinate-transforms"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tf-blender-temporal-feature-blender-for-video","slug":"tf-blender-temporal-feature-blender-for-video","title":"TF-Blender: Temporal Feature Blender for Video Object Detection","date":"2021-08-12","arxiv_id":"2108.05821","repositories_listed":1,"syntology":null},{"url":"/paper/fog-simulation-on-real-lidar-point-clouds-for","slug":"fog-simulation-on-real-lidar-point-clouds-for","title":"Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather","date":"2021-08-11","arxiv_id":"2108.05249","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"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) · 5 unverified","sample_list":"/paper/fog-simulation-on-real-lidar-point-clouds-for#ran","syntology_url":"https://syntology.ai/paper/2108.05249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05249"}},"official":{"repos":["MartinHahner/LiDAR_fog_sim"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/mining-the-benefits-of-two-stage-and-one","slug":"mining-the-benefits-of-two-stage-and-one","title":"Mining the Benefits of Two-stage and One-stage HOI Detection","date":"2021-08-11","arxiv_id":"2108.05077","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mining-the-benefits-of-two-stage-and-one#ran","syntology_url":"https://syntology.ai/paper/2108.05077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05077"}},"official":{"repos":["YueLiao/CDN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-multi-object-detection-and-tracking","slug":"joint-multi-object-detection-and-tracking","title":"Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving","date":"2021-08-10","arxiv_id":"2108.04602","repositories_listed":1,"syntology":null},{"url":"/paper/uninet-a-unified-scene-understanding-network","slug":"uninet-a-unified-scene-understanding-network","title":"UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks","date":"2021-08-10","arxiv_id":"2108.04584","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/uninet-a-unified-scene-understanding-network#ran","syntology_url":"https://syntology.ai/paper/2108.04584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04584"}},"official":{"repos":["NeurAI-Lab/UniNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tritransnet-rgb-d-salient-object-detection","slug":"tritransnet-rgb-d-salient-object-detection","title":"TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network","date":"2021-08-09","arxiv_id":"2108.03990","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":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tritransnet-rgb-d-salient-object-detection#ran","syntology_url":"https://syntology.ai/paper/2108.03990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03990"}},"official":{"repos":["liuzywen/tritransnet"],"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/from-voxel-to-point-iou-guided-3d-object","slug":"from-voxel-to-point-iou-guided-3d-object","title":"From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder","date":"2021-08-08","arxiv_id":"2108.03648","repositories_listed":1,"syntology":null},{"url":"/paper/mpi-multi-receptive-and-parallel-integration","slug":"mpi-multi-receptive-and-parallel-integration","title":"MPI: Multi-receptive and Parallel Integration for Salient Object Detection","date":"2021-08-08","arxiv_id":"2108.03618","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-adversarial-attacks-on-driving","slug":"evaluating-adversarial-attacks-on-driving","title":"Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles","date":"2021-08-06","arxiv_id":"2108.02940","repositories_listed":1,"syntology":null},{"url":"/paper/fast-convergence-of-detr-with-spatially-1","slug":"fast-convergence-of-detr-with-spatially-1","title":"Fast Convergence of DETR with Spatially Modulated Co-Attention","date":"2021-08-05","arxiv_id":"2108.02404","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-global-local-representations-in","slug":"unifying-global-local-representations-in","title":"Unifying Global-Local Representations in Salient Object Detection with Transformer","date":"2021-08-05","arxiv_id":"2108.02759","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modality-discrepant-interaction-network","slug":"cross-modality-discrepant-interaction-network","title":"Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection","date":"2021-08-04","arxiv_id":"2108.01971","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-relevance-learning-for-few-shot","slug":"dynamic-relevance-learning-for-few-shot","title":"Dynamic Relevance Learning for Few-Shot Object Detection","date":"2021-08-04","arxiv_id":"2108.02235","repositories_listed":1,"syntology":null},{"url":"/paper/fpb-feature-pyramid-branch-for-person-re","slug":"fpb-feature-pyramid-branch-for-person-re","title":"FPB: Feature Pyramid Branch for Person Re-Identification","date":"2021-08-04","arxiv_id":"2108.01901","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-weakly-supervised-object-detection-1","slug":"boosting-weakly-supervised-object-detection-1","title":"Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters","date":"2021-08-03","arxiv_id":"2108.01499","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":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/boosting-weakly-supervised-object-detection-1#ran","syntology_url":"https://syntology.ai/paper/2108.01499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01499"}},"official":{"repos":["DongSky/lbba_boosted_wsod"],"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/i2v-gan-unpaired-infrared-to-visible-video","slug":"i2v-gan-unpaired-infrared-to-visible-video","title":"I2V-GAN: Unpaired Infrared-to-Visible Video Translation","date":"2021-08-02","arxiv_id":"2108.00913","repositories_listed":1,"syntology":null},{"url":"/paper/identify-light-curve-signals-with-deep","slug":"identify-light-curve-signals-with-deep","title":"Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection","date":"2021-08-02","arxiv_id":"2108.00670","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-attention-mechanism-in-3d-point","slug":"investigating-attention-mechanism-in-3d-point","title":"Investigating Attention Mechanism in 3D Point Cloud Object Detection","date":"2021-08-02","arxiv_id":"2108.00620","repositories_listed":1,"syntology":null},{"url":"/paper/dpt-deformable-patch-based-transformer-for","slug":"dpt-deformable-patch-based-transformer-for","title":"DPT: Deformable Patch-based Transformer for Visual Recognition","date":"2021-07-30","arxiv_id":"2107.14467","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/dpt-deformable-patch-based-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2107.14467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.14467"}},"official":{"repos":["CASIA-IVA-Lab/DPT"],"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/from-multi-view-to-hollow-3d-hallucinated","slug":"from-multi-view-to-hollow-3d-hallucinated","title":"From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection","date":"2021-07-30","arxiv_id":"2107.14391","repositories_listed":1,"syntology":null},{"url":"/paper/geometry-uncertainty-projection-network-for","slug":"geometry-uncertainty-projection-network-for","title":"Geometry Uncertainty Projection Network for Monocular 3D Object Detection","date":"2021-07-29","arxiv_id":"2107.13774","repositories_listed":1,"syntology":{"n":21,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":2,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/geometry-uncertainty-projection-network-for#ran","syntology_url":"https://syntology.ai/paper/2107.13774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13774"}},"official":{"repos":["supermhp/gupnet"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-geometry-guided-depth-via-projective","slug":"learning-geometry-guided-depth-via-projective","title":"Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection","date":"2021-07-29","arxiv_id":"2107.13931","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-and-geometric-depth-detecting","slug":"probabilistic-and-geometric-depth-detecting","title":"Probabilistic and Geometric Depth: Detecting Objects in Perspective","date":"2021-07-29","arxiv_id":"2107.14160","repositories_listed":1,"syntology":null},{"url":"/paper/united-we-learn-better-harvesting-learning","slug":"united-we-learn-better-harvesting-learning","title":"United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks","date":"2021-07-28","arxiv_id":"2107.13627","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-sequence-feature-alignment-for","slug":"exploring-sequence-feature-alignment-for","title":"Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers","date":"2021-07-27","arxiv_id":"2107.12636","repositories_listed":1,"syntology":null},{"url":"/paper/image-scene-graph-generation-sgg-benchmark","slug":"image-scene-graph-generation-sgg-benchmark","title":"Image Scene Graph Generation (SGG) Benchmark","date":"2021-07-27","arxiv_id":"2107.12604","repositories_listed":1,"syntology":null},{"url":"/paper/workshop-on-autonomous-driving-at-cvpr-2021","slug":"workshop-on-autonomous-driving-at-cvpr-2021","title":"Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge","date":"2021-07-27","arxiv_id":"2108.04230","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/workshop-on-autonomous-driving-at-cvpr-2021#ran","syntology_url":"https://syntology.ai/paper/2108.04230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04230"}},"official":{"repos":["Megvii-BaseDetection/YOLOX"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sarnet-a-dataset-for-deep-learning-assisted","slug":"sarnet-a-dataset-for-deep-learning-assisted","title":"SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery","date":"2021-07-26","arxiv_id":"2107.12469","repositories_listed":1,"syntology":null},{"url":"/paper/asod60k-audio-induced-salient-object","slug":"asod60k-audio-induced-salient-object","title":"ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic Videos","date":"2021-07-24","arxiv_id":"2107.11629","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-domain-adaptive-3d-detection","slug":"unsupervised-domain-adaptive-3d-detection","title":"Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency","date":"2021-07-23","arxiv_id":"2107.11355","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"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 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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/unsupervised-domain-adaptive-3d-detection#ran","syntology_url":"https://syntology.ai/paper/2107.11355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11355"}},"official":null}},{"url":"/paper/weighted-intersection-over-union-wiou-a-new","slug":"weighted-intersection-over-union-wiou-a-new","title":"Weighted Intersection over Union (wIoU) for Evaluating Image Segmentation","date":"2021-07-21","arxiv_id":"2107.09858","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-and-explainable-grading-of","slug":"automatic-and-explainable-grading-of","title":"Automatic and explainable grading of meningiomas from histopathology images","date":"2021-07-19","arxiv_id":"2107.08850","repositories_listed":1,"syntology":null},{"url":"/paper/global-object-proposals-for-improving-multi","slug":"global-object-proposals-for-improving-multi","title":"Global Object Proposals for Improving Multi-Sentence Video Descriptions","date":"2021-07-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/class-agnostic-segmentation-loss-and-its-1","slug":"class-agnostic-segmentation-loss-and-its-1","title":"Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation","date":"2021-07-16","arxiv_id":"2108.04226","repositories_listed":1,"syntology":null},{"url":"/paper/doremi-first-glance-at-a-universal-omr","slug":"doremi-first-glance-at-a-universal-omr","title":"DoReMi: First glance at a universal OMR dataset","date":"2021-07-16","arxiv_id":"2107.07786","repositories_listed":1,"syntology":null},{"url":"/paper/lidar-light-scattering-augmentation-lisa","slug":"lidar-light-scattering-augmentation-lisa","title":"Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection","date":"2021-07-14","arxiv_id":"2107.07004","repositories_listed":1,"syntology":null},{"url":"/paper/geographical-knowledge-driven-representation","slug":"geographical-knowledge-driven-representation","title":"Geographical Knowledge-driven Representation Learning for Remote Sensing Images","date":"2021-07-12","arxiv_id":"2107.05276","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-surface-uncertainty-quantification","slug":"prediction-surface-uncertainty-quantification","title":"Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving","date":"2021-07-11","arxiv_id":"2107.04991","repositories_listed":1,"syntology":null},{"url":"/paper/csl-yolo-a-new-lightweight-object-detection","slug":"csl-yolo-a-new-lightweight-object-detection","title":"CSL-YOLO: A New Lightweight Object Detection System for Edge Computing","date":"2021-07-10","arxiv_id":"2107.04829","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-based-quantification-of-epistemic","slug":"gradient-based-quantification-of-epistemic","title":"Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors","date":"2021-07-09","arxiv_id":"2107.04517","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modality-task-cascade-for-3d-object","slug":"multi-modality-task-cascade-for-3d-object","title":"Multi-Modality Task Cascade for 3D Object Detection","date":"2021-07-08","arxiv_id":"2107.04013","repositories_listed":1,"syntology":null},{"url":"/paper/neighbor-vote-improving-monocular-3d-object","slug":"neighbor-vote-improving-monocular-3d-object","title":"Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting","date":"2021-07-06","arxiv_id":"2107.02493","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neighbor-vote-improving-monocular-3d-object#ran","syntology_url":"https://syntology.ai/paper/2107.02493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02493"}},"official":null}},{"url":"/paper/depth-quality-inspired-feature-manipulation","slug":"depth-quality-inspired-feature-manipulation","title":"Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection","date":"2021-07-05","arxiv_id":"2107.01779","repositories_listed":1,"syntology":null},{"url":"/paper/on-model-calibration-for-long-tailed-object","slug":"on-model-calibration-for-long-tailed-object","title":"On Model Calibration for Long-Tailed Object Detection and Instance Segmentation","date":"2021-07-05","arxiv_id":"2107.02170","repositories_listed":1,"syntology":null},{"url":"/paper/divergentnets-medical-image-segmentation-by","slug":"divergentnets-medical-image-segmentation-by","title":"DivergentNets: Medical Image Segmentation by Network Ensemble","date":"2021-07-01","arxiv_id":"2107.00283","repositories_listed":1,"syntology":null},{"url":"/paper/mass-multi-attentional-semantic-segmentation","slug":"mass-multi-attentional-semantic-segmentation","title":"MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding","date":"2021-07-01","arxiv_id":"2107.00346","repositories_listed":1,"syntology":null},{"url":"/paper/simnet-enabling-robust-unknown-object","slug":"simnet-enabling-robust-unknown-object","title":"SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo","date":"2021-06-30","arxiv_id":"2106.16118","repositories_listed":1,"syntology":null},{"url":"/paper/simple-training-strategies-and-model-scaling","slug":"simple-training-strategies-and-model-scaling","title":"Simple Training Strategies and Model Scaling for Object Detection","date":"2021-06-30","arxiv_id":"2107.00057","repositories_listed":1,"syntology":null},{"url":"/paper/rcnn-slicenet-a-slice-and-cluster-approach","slug":"rcnn-slicenet-a-slice-and-cluster-approach","title":"RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images","date":"2021-06-29","arxiv_id":"2106.15753","repositories_listed":1,"syntology":null},{"url":"/paper/simpl-generating-synthetic-overhead-imagery","slug":"simpl-generating-synthetic-overhead-imagery","title":"SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems","date":"2021-06-29","arxiv_id":"2106.15681","repositories_listed":1,"syntology":null},{"url":"/paper/an-uncertainty-estimation-framework-for","slug":"an-uncertainty-estimation-framework-for","title":"An Uncertainty Estimation Framework for Probabilistic Object Detection","date":"2021-06-28","arxiv_id":"2106.15007","repositories_listed":1,"syntology":null},{"url":"/paper/inverting-and-understanding-object-detectors","slug":"inverting-and-understanding-object-detectors","title":"Inverting and Understanding Object Detectors","date":"2021-06-26","arxiv_id":"2106.13933","repositories_listed":1,"syntology":null},{"url":"/paper/radar-voxel-fusion-for-3d-object-detection","slug":"radar-voxel-fusion-for-3d-object-detection","title":"Radar Voxel Fusion for 3D Object Detection","date":"2021-06-26","arxiv_id":"2106.14087","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-3d-object-detection-using-feature","slug":"real-time-3d-object-detection-using-feature","title":"Real-time 3D Object Detection using Feature Map Flow","date":"2021-06-26","arxiv_id":"2106.14101","repositories_listed":1,"syntology":null},{"url":"/paper/a-cnn-segmentation-based-approach-to-object","slug":"a-cnn-segmentation-based-approach-to-object","title":"A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection","date":"2021-06-25","arxiv_id":"2106.13849","repositories_listed":1,"syntology":null},{"url":"/paper/srpn-similarity-based-region-proposal","slug":"srpn-similarity-based-region-proposal","title":"SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images","date":"2021-06-25","arxiv_id":"2106.13556","repositories_listed":1,"syntology":null},{"url":"/paper/fusionpainting-multimodal-fusion-with","slug":"fusionpainting-multimodal-fusion-with","title":"FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection","date":"2021-06-23","arxiv_id":"2106.12449","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-aware-learning-for-camouflaged","slug":"confidence-aware-learning-for-camouflaged","title":"Confidence-Aware Learning for Camouflaged Object Detection","date":"2021-06-22","arxiv_id":"2106.11641","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-instances-as-queries","slug":"tracking-instances-as-queries","title":"Tracking Instances as Queries","date":"2021-06-22","arxiv_id":"2106.11963","repositories_listed":1,"syntology":null},{"url":"/paper/3d-object-detection-for-autonomous-driving-a","slug":"3d-object-detection-for-autonomous-driving-a","title":"3D Object Detection for Autonomous Driving: A Survey","date":"2021-06-21","arxiv_id":"2106.10823","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/3d-object-detection-for-autonomous-driving-a#ran","syntology_url":"https://syntology.ai/paper/2106.10823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10823"}},"official":{"repos":["rui-qian/SoTA-3D-Object-Detection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gaia-a-transfer-learning-system-of-object","slug":"gaia-a-transfer-learning-system-of-object","title":"GAIA: A Transfer Learning System of Object Detection that Fits Your Needs","date":"2021-06-21","arxiv_id":"2106.11346","repositories_listed":1,"syntology":null}],"record_sha256":"6fe0f477f7875106aa6b4e85494ad4603bfacc26c121f47fa27f9b46672c841b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}