{"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/papers/40","list_of":"/task/object-detection","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":40,"pages_in_order":110,"rows_per_page":100,"rows":[3901,4000],"of":10957,"counts":{"archive_papers_tagged":10957,"with_a_code_link":4657,"where_syntology_ran_a_sample":1183,"not_listed_spam_title":0,"listed":10957,"listed_where_code_ran":1183,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":145,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":145,"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","prev":"/task/object-detection/papers/39","next":"/task/object-detection/papers/41","papers":[{"url":"/paper/hrcenternet-an-anchorless-approach-to-chinese","slug":"hrcenternet-an-anchorless-approach-to-chinese","title":"HRCenterNet: An Anchorless Approach to Chinese Character Segmentation in Historical Documents","date":"2020-12-10","arxiv_id":"2012.05739","repositories_listed":1,"syntology":null},{"url":"/paper/onenet-towards-end-to-end-one-stage-object","slug":"onenet-towards-end-to-end-one-stage-object","title":"What Makes for End-to-End Object Detection?","date":"2020-12-10","arxiv_id":"2012.05780","repositories_listed":1,"syntology":null},{"url":"/paper/ds-net-dynamic-spatiotemporal-network-for","slug":"ds-net-dynamic-spatiotemporal-network-for","title":"DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection","date":"2020-12-09","arxiv_id":"2012.04886","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-3d-object-detection-using-energy","slug":"accurate-3d-object-detection-using-energy","title":"Accurate 3D Object Detection using Energy-Based Models","date":"2020-12-08","arxiv_id":"2012.04634","repositories_listed":1,"syntology":null},{"url":"/paper/structure-consistent-weakly-supervised","slug":"structure-consistent-weakly-supervised","title":"Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence","date":"2020-12-08","arxiv_id":"2012.04404","repositories_listed":1,"syntology":null},{"url":"/paper/the-lottery-ticket-hypothesis-for-object","slug":"the-lottery-ticket-hypothesis-for-object","title":"The Lottery Ticket Hypothesis for Object Recognition","date":"2020-12-08","arxiv_id":"2012.04643","repositories_listed":1,"syntology":null},{"url":"/paper/using-feature-alignment-can-improve-clean","slug":"using-feature-alignment-can-improve-clean","title":"Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection","date":"2020-12-08","arxiv_id":"2012.04382","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-object-detection-with-fully","slug":"end-to-end-object-detection-with-fully","title":"End-to-End Object Detection with Fully Convolutional Network","date":"2020-12-07","arxiv_id":"2012.03544","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-dynamic-head-for-object-1","slug":"fine-grained-dynamic-head-for-object-1","title":"Fine-Grained Dynamic Head for Object Detection","date":"2020-12-07","arxiv_id":"2012.03519","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-learnable-tree-filter-for-generic-1","slug":"rethinking-learnable-tree-filter-for-generic-1","title":"Rethinking Learnable Tree Filter for Generic Feature Transform","date":"2020-12-07","arxiv_id":"2012.03482","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-object-detection-in-scale","slug":"towards-better-object-detection-in-scale","title":"Towards Better Object Detection in Scale Variation with Adaptive Feature Selection","date":"2020-12-06","arxiv_id":"2012.03265","repositories_listed":1,"syntology":null},{"url":"/paper/co-mining-self-supervised-learning-for","slug":"co-mining-self-supervised-learning-for","title":"Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection","date":"2020-12-03","arxiv_id":"2012.01950","repositories_listed":1,"syntology":null},{"url":"/paper/parallel-residual-bi-fusion-feature-pyramid","slug":"parallel-residual-bi-fusion-feature-pyramid","title":"Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection","date":"2020-12-03","arxiv_id":"2012.01724","repositories_listed":1,"syntology":null},{"url":"/paper/auto-learning-attention","slug":"auto-learning-attention","title":"Auto Learning Attention","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-feature-pyramid-networks-for-object","slug":"dynamic-feature-pyramid-networks-for-object","title":"Dynamic Feature Pyramid Networks for Object Detection","date":"2020-12-01","arxiv_id":"2012.00779","repositories_listed":1,"syntology":null},{"url":"/paper/is-normalization-indispensable-for-training","slug":"is-normalization-indispensable-for-training","title":"Is normalization indispensable for training deep neural network?","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uwsod-toward-fully-supervised-level-capacity","slug":"uwsod-toward-fully-supervised-level-capacity","title":"UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/monocular-3d-object-detection-with-sequential","slug":"monocular-3d-object-detection-with-sequential","title":"Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation","date":"2020-11-30","arxiv_id":"2011.14589","repositories_listed":1,"syntology":null},{"url":"/paper/move-to-see-better-towards-self-supervised","slug":"move-to-see-better-towards-self-supervised","title":"Move to See Better: Self-Improving Embodied Object Detection","date":"2020-11-30","arxiv_id":"2012.00057","repositories_listed":1,"syntology":null},{"url":"/paper/rfd-net-point-scene-understanding-by-semantic","slug":"rfd-net-point-scene-understanding-by-semantic","title":"RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction","date":"2020-11-30","arxiv_id":"2011.14744","repositories_listed":1,"syntology":null},{"url":"/paper/cminmax-a-fast-algorithm-to-find-the-corners","slug":"cminmax-a-fast-algorithm-to-find-the-corners","title":"cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope","date":"2020-11-28","arxiv_id":"2011.14035","repositories_listed":1,"syntology":null},{"url":"/paper/how-well-do-self-supervised-models-transfer","slug":"how-well-do-self-supervised-models-transfer","title":"How Well Do Self-Supervised Models Transfer?","date":"2020-11-26","arxiv_id":"2011.13377","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-well-do-self-supervised-models-transfer#ran","syntology_url":"https://syntology.ai/paper/2011.13377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13377"}},"official":{"repos":["linusericsson/ssl-transfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-region-proposal-learning-for-object","slug":"fast-region-proposal-learning-for-object","title":"Fast Region Proposal Learning for Object Detection for Robotics","date":"2020-11-25","arxiv_id":"2011.12790","repositories_listed":1,"syntology":null},{"url":"/paper/torchdistill-a-modular-configuration-driven","slug":"torchdistill-a-modular-configuration-driven","title":"torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation","date":"2020-11-25","arxiv_id":"2011.12913","repositories_listed":1,"syntology":{"n":26,"n_ran":10,"n_constructed":0,"n_ran_checked":0,"n_instrument":10,"n_unverified":16,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"10 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; 10 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/torchdistill-a-modular-configuration-driven#ran","syntology_url":"https://syntology.ai/paper/2011.12913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12913"}},"official":{"repos":["yoshitomo-matsubara/torchdistill"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":16,"ran_from_kinds":["official"]}}},{"url":"/paper/canonical-voting-towards-robust-oriented","slug":"canonical-voting-towards-robust-oriented","title":"Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes","date":"2020-11-24","arxiv_id":"2011.12001","repositories_listed":1,"syntology":null},{"url":"/paper/keepaugment-a-simple-information-preserving","slug":"keepaugment-a-simple-information-preserving","title":"KeepAugment: A Simple Information-Preserving Data Augmentation Approach","date":"2020-11-23","arxiv_id":"2011.11778","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-neural-network-improves","slug":"object-detection-neural-network-improves","title":"Object detection neural network improves Fourier ptychography reconstruction","date":"2020-11-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/structure-aware-completion-of-photogrammetric","slug":"structure-aware-completion-of-photogrammetric","title":"Structure-Aware Completion of Photogrammetric Meshes in Urban Road Environment","date":"2020-11-23","arxiv_id":"2011.11210","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-transformer-based-set-prediction","slug":"rethinking-transformer-based-set-prediction","title":"Rethinking Transformer-based Set Prediction for Object Detection","date":"2020-11-21","arxiv_id":"2011.10881","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-transformer-based-set-prediction#ran","syntology_url":"https://syntology.ai/paper/2011.10881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10881"}},"official":{"repos":["edward-sun/tsp-detection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-review-and-comparative-study-on","slug":"a-review-and-comparative-study-on","title":"A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving","date":"2020-11-20","arxiv_id":"2011.10671","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-review-and-comparative-study-on#ran","syntology_url":"https://syntology.ai/paper/2011.10671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10671"}},"official":{"repos":["asharakeh/pod_compare"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-representation-of-temporal-image","slug":"joint-representation-of-temporal-image","title":"Joint Representation of Temporal Image Sequences and Object Motion for Video Object Detection","date":"2020-11-20","arxiv_id":"2011.10278","repositories_listed":1,"syntology":null},{"url":"/paper/open-vocabulary-object-detection-using","slug":"open-vocabulary-object-detection-using","title":"Open-Vocabulary Object Detection Using Captions","date":"2020-11-20","arxiv_id":"2011.10678","repositories_listed":1,"syntology":null},{"url":"/paper/geography-aware-self-supervised-learning","slug":"geography-aware-self-supervised-learning","title":"Geography-Aware Self-Supervised Learning","date":"2020-11-19","arxiv_id":"2011.09980","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/geography-aware-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2011.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.09980"}},"official":{"repos":["sustainlab-group/geography-aware-ssl"],"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/end-to-end-object-detection-with-adaptive","slug":"end-to-end-object-detection-with-adaptive","title":"End-to-End Object Detection with Adaptive Clustering Transformer","date":"2020-11-18","arxiv_id":"2011.09315","repositories_listed":1,"syntology":null},{"url":"/paper/tju-dhd-a-diverse-high-resolution-dataset-for","slug":"tju-dhd-a-diverse-high-resolution-dataset-for","title":"TJU-DHD: A Diverse High-Resolution Dataset for Object Detection","date":"2020-11-18","arxiv_id":"2011.09170","repositories_listed":1,"syntology":null},{"url":"/paper/slender-object-detection-diagnoses-and","slug":"slender-object-detection-diagnoses-and","title":"Slender Object Detection: Diagnoses and Improvements","date":"2020-11-17","arxiv_id":"2011.08529","repositories_listed":1,"syntology":null},{"url":"/paper/amphibiandetector-adaptive-computation-for","slug":"amphibiandetector-adaptive-computation-for","title":"AmphibianDetector: adaptive computation for moving objects detection","date":"2020-11-15","arxiv_id":"2011.07513","repositories_listed":1,"syntology":null},{"url":"/paper/gndnet-fast-ground-plane-estimation-and-point","slug":"gndnet-fast-ground-plane-estimation-and-point","title":"GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles","date":"2020-11-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/svam-saliency-guided-visual-attention","slug":"svam-saliency-guided-visual-attention","title":"SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots","date":"2020-11-12","arxiv_id":"2011.06252","repositories_listed":1,"syntology":null},{"url":"/paper/littleyolo-spp-a-delicate-real-time-vehicle","slug":"littleyolo-spp-a-delicate-real-time-vehicle","title":"LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm","date":"2020-11-11","arxiv_id":"2011.05940","repositories_listed":1,"syntology":null},{"url":"/paper/optimized-loss-functions-for-object-detection","slug":"optimized-loss-functions-for-object-detection","title":"Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection","date":"2020-11-11","arxiv_id":"2011.05523","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-supervised-learning-system-for-object-1","slug":"a-self-supervised-learning-system-for-object-1","title":"A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs","date":"2020-11-10","arxiv_id":"2011.05459","repositories_listed":1,"syntology":null},{"url":"/paper/coadnet-collaborative-aggregation-and","slug":"coadnet-collaborative-aggregation-and","title":"CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection","date":"2020-11-10","arxiv_id":"2011.04887","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-active-search-using-realistic","slug":"multi-agent-active-search-using-realistic","title":"Multi-Agent Active Search using Realistic Depth-Aware Noise Model","date":"2020-11-09","arxiv_id":"2011.04825","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-object-detection-method-based-on","slug":"real-time-object-detection-method-based-on","title":"Real-time object detection method based on improved YOLOv4-tiny","date":"2020-11-09","arxiv_id":"2011.04244","repositories_listed":1,"syntology":null},{"url":"/paper/towards-resolving-the-challenge-of-long-tail","slug":"towards-resolving-the-challenge-of-long-tail","title":"Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection","date":"2020-11-07","arxiv_id":"2011.03822","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-3d-prototypical-networks-for-1","slug":"disentangling-3d-prototypical-networks-for-1","title":"Disentangling 3D Prototypical Networks For Few-Shot Concept Learning","date":"2020-11-06","arxiv_id":"2011.03367","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-geometric-representation-for-data","slug":"learning-a-geometric-representation-for-data","title":"Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss","date":"2020-11-06","arxiv_id":"2011.03207","repositories_listed":1,"syntology":null},{"url":"/paper/towards-efficient-scene-understanding-via","slug":"towards-efficient-scene-understanding-via","title":"Towards Efficient Scene Understanding via Squeeze Reasoning","date":"2020-11-06","arxiv_id":"2011.03308","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-efficient-scene-understanding-via#ran","syntology_url":"https://syntology.ai/paper/2011.03308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.03308"}},"official":{"repos":["lxtGH/SFSegNets"],"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","unlocated"]}}},{"url":"/paper/deep-dup-an-adversarial-weight-duplication","slug":"deep-dup-an-adversarial-weight-duplication","title":"Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA","date":"2020-11-05","arxiv_id":"2011.03006","repositories_listed":1,"syntology":null},{"url":"/paper/ef-net-a-novel-enhancement-and-fusion-network","slug":"ef-net-a-novel-enhancement-and-fusion-network","title":"EF-Net: A novel enhancement and fusion network for RGB-D saliency detection","date":"2020-11-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-voxel-based-3d-object","slug":"uncertainty-aware-voxel-based-3d-object","title":"Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss","date":"2020-11-04","arxiv_id":"2011.02553","repositories_listed":1,"syntology":null},{"url":"/paper/faraway-frustum-dealing-with-lidar-sparsity","slug":"faraway-frustum-dealing-with-lidar-sparsity","title":"Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion","date":"2020-11-03","arxiv_id":"2011.01404","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/faraway-frustum-dealing-with-lidar-sparsity#ran","syntology_url":"https://syntology.ai/paper/2011.01404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01404"}},"official":{"repos":["dongfang-steven-yang/faraway-frustum"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-visual-representations-for-transfer-1","slug":"learning-visual-representations-for-transfer-1","title":"Learning Visual Representations for Transfer Learning by Suppressing Texture","date":"2020-11-03","arxiv_id":"2011.01901","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-adaptive-fusion-network-for-3d","slug":"multi-view-adaptive-fusion-network-for-3d","title":"Multi-View Adaptive Fusion Network for 3D Object Detection","date":"2020-11-02","arxiv_id":"2011.00652","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptive-object-detection-via-1","slug":"domain-adaptive-object-detection-via-1","title":"Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment","date":"2020-10-31","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pose-randomization-for-weakly-paired-image","slug":"pose-randomization-for-weakly-paired-image","title":"PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation","date":"2020-10-31","arxiv_id":"2011.00301","repositories_listed":1,"syntology":null},{"url":"/paper/domain-specific-lexical-grounding-in-noisy","slug":"domain-specific-lexical-grounding-in-noisy","title":"Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents","date":"2020-10-30","arxiv_id":"2010.16363","repositories_listed":1,"syntology":null},{"url":"/paper/permute-quantize-and-fine-tune-efficient","slug":"permute-quantize-and-fine-tune-efficient","title":"Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks","date":"2020-10-29","arxiv_id":"2010.15703","repositories_listed":1,"syntology":null},{"url":"/paper/class-agnostic-segmentation-loss-and-its","slug":"class-agnostic-segmentation-loss-and-its","title":"Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation","date":"2020-10-28","arxiv_id":"2010.14793","repositories_listed":1,"syntology":null},{"url":"/paper/object-hider-adversarial-patch-attack-against","slug":"object-hider-adversarial-patch-attack-against","title":"Object Hider: Adversarial Patch Attack Against Object Detectors","date":"2020-10-28","arxiv_id":"2010.14974","repositories_listed":1,"syntology":null},{"url":"/paper/perception-for-autonomous-systems-paz","slug":"perception-for-autonomous-systems-paz","title":"Perception for Autonomous Systems (PAZ)","date":"2020-10-27","arxiv_id":"2010.14541","repositories_listed":1,"syntology":null},{"url":"/paper/gan-mask-r-cnn-instance-semantic-segmentation","slug":"gan-mask-r-cnn-instance-semantic-segmentation","title":"Instance Semantic Segmentation Benefits from Generative Adversarial Networks","date":"2020-10-26","arxiv_id":"2010.13757","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":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gan-mask-r-cnn-instance-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2010.13757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13757"}},"official":{"repos":["quangle2110/GAN_Mask-RCNN"],"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/video-understanding-based-on-human-action-and","slug":"video-understanding-based-on-human-action-and","title":"Improved Actor Relation Graph based Group Activity Recognition","date":"2020-10-24","arxiv_id":"2010.12968","repositories_listed":1,"syntology":null},{"url":"/paper/comprehensive-attention-self-distillation-for","slug":"comprehensive-attention-self-distillation-for","title":"Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection","date":"2020-10-22","arxiv_id":"2010.12023","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/comprehensive-attention-self-distillation-for#ran","syntology_url":"https://syntology.ai/paper/2010.12023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12023"}},"official":{"repos":["DeLightCMU/CASD"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/restoring-negative-information-in-few-shot","slug":"restoring-negative-information-in-few-shot","title":"Restoring Negative Information in Few-Shot Object Detection","date":"2020-10-22","arxiv_id":"2010.11714","repositories_listed":1,"syntology":null},{"url":"/paper/approxdet-content-and-contention-aware","slug":"approxdet-content-and-contention-aware","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","date":"2020-10-21","arxiv_id":"2010.10754","repositories_listed":1,"syntology":null},{"url":"/paper/ivadomed-a-medical-imaging-deep-learning","slug":"ivadomed-a-medical-imaging-deep-learning","title":"ivadomed: A Medical Imaging Deep Learning Toolbox","date":"2020-10-20","arxiv_id":"2010.09984","repositories_listed":1,"syntology":null},{"url":"/paper/ttpla-an-aerial-image-dataset-for-detection","slug":"ttpla-an-aerial-image-dataset-for-detection","title":"TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines","date":"2020-10-20","arxiv_id":"2010.10032","repositories_listed":1,"syntology":null},{"url":"/paper/the-efficacy-of-neural-planning-metrics-a","slug":"the-efficacy-of-neural-planning-metrics-a","title":"The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes","date":"2020-10-19","arxiv_id":"2010.09350","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-efficacy-of-neural-planning-metrics-a#ran","syntology_url":"https://syntology.ai/paper/2010.09350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09350"}},"official":null}},{"url":"/paper/radiate-a-radar-dataset-for-automotive","slug":"radiate-a-radar-dataset-for-automotive","title":"RADIATE: A Radar Dataset for Automotive Perception in Bad Weather","date":"2020-10-18","arxiv_id":"2010.09076","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":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) · 0 unverified","sample_list":"/paper/radiate-a-radar-dataset-for-automotive#ran","syntology_url":"https://syntology.ai/paper/2010.09076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09076"}},"official":{"repos":["marcelsheeny/radiate_sdk"],"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/manipulation-oriented-object-perception-in","slug":"manipulation-oriented-object-perception-in","title":"Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames","date":"2020-10-16","arxiv_id":"2010.08202","repositories_listed":1,"syntology":null},{"url":"/paper/sf-uda-3d-source-free-unsupervised-domain","slug":"sf-uda-3d-source-free-unsupervised-domain","title":"SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection","date":"2020-10-16","arxiv_id":"2010.08243","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-object-detection","slug":"privacy-preserving-object-detection","title":"Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning","date":"2020-10-14","arxiv_id":"2010.07259","repositories_listed":1,"syntology":null},{"url":"/paper/learning-selective-mutual-attention-and","slug":"learning-selective-mutual-attention-and","title":"Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection","date":"2020-10-12","arxiv_id":"2010.05537","repositories_listed":1,"syntology":null},{"url":"/paper/the-meccano-dataset-understanding-human","slug":"the-meccano-dataset-understanding-human","title":"The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain","date":"2020-10-12","arxiv_id":"2010.05654","repositories_listed":1,"syntology":null},{"url":"/paper/light-field-salient-object-detection-a-review","slug":"light-field-salient-object-detection-a-review","title":"Light Field Salient Object Detection: A Review and Benchmark","date":"2020-10-10","arxiv_id":"2010.04968","repositories_listed":1,"syntology":null},{"url":"/paper/background-learnable-cascade-for-zero-shot","slug":"background-learnable-cascade-for-zero-shot","title":"Background Learnable Cascade for Zero-Shot Object Detection","date":"2020-10-09","arxiv_id":"2010.04502","repositories_listed":1,"syntology":null},{"url":"/paper/single-stage-rotation-decoupled-detector-for","slug":"single-stage-rotation-decoupled-detector-for","title":"Single-Stage Rotation-Decoupled Detector for Oriented Object","date":"2020-10-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vision-based-object-recognition-in-indoor","slug":"vision-based-object-recognition-in-indoor","title":"Visual Object Recognition in Indoor Environments Using Topologically Persistent Features","date":"2020-10-07","arxiv_id":"2010.03196","repositories_listed":1,"syntology":null},{"url":"/paper/blendtorch-a-real-time-adaptive-domain","slug":"blendtorch-a-real-time-adaptive-domain","title":"BlendTorch: A Real-Time, Adaptive Domain Randomization Library","date":"2020-10-06","arxiv_id":"2010.11696","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-automotive-radar-data-acquisition-1","slug":"adaptive-automotive-radar-data-acquisition-1","title":"Automotive Radar Data Acquisition using Object Detection","date":"2020-10-05","arxiv_id":"2010.02367","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-negative-sampling-for-contrastive-1","slug":"conditional-negative-sampling-for-contrastive-1","title":"Conditional Negative Sampling for Contrastive Learning of Visual Representations","date":"2020-10-05","arxiv_id":"2010.02037","repositories_listed":1,"syntology":null},{"url":"/paper/mind-the-pad-cnns-can-develop-blind-spots-1","slug":"mind-the-pad-cnns-can-develop-blind-spots-1","title":"Mind the Pad -- CNNs can Develop Blind Spots","date":"2020-10-05","arxiv_id":"2010.02178","repositories_listed":1,"syntology":null},{"url":"/paper/olala-object-level-active-learning-based","slug":"olala-object-level-active-learning-based","title":"OLALA: Object-Level Active Learning for Efficient Document Layout Annotation","date":"2020-10-05","arxiv_id":"2010.01762","repositories_listed":1,"syntology":null},{"url":"/paper/metadetect-uncertainty-quantification-and","slug":"metadetect-uncertainty-quantification-and","title":"MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection","date":"2020-10-04","arxiv_id":"2010.01695","repositories_listed":1,"syntology":null},{"url":"/paper/nonconvex-regularization-for-network-slimming","slug":"nonconvex-regularization-for-network-slimming","title":"Improving Network Slimming with Nonconvex Regularization","date":"2020-10-03","arxiv_id":"2010.01242","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/nonconvex-regularization-for-network-slimming#ran","syntology_url":"https://syntology.ai/paper/2010.01242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01242"}},"official":{"repos":["kbui1993/NonconvexNetworkSlimming"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/hard-negative-mixing-for-contrastive-learning","slug":"hard-negative-mixing-for-contrastive-learning","title":"Hard Negative Mixing for Contrastive Learning","date":"2020-10-02","arxiv_id":"2010.01028","repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-efficient-post-processing-for","slug":"robust-and-efficient-post-processing-for","title":"Robust and Efficient Post-Processing for Video Object Detection (REPP)","date":"2020-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/localize-to-classify-and-classify-to-localize","slug":"localize-to-classify-and-classify-to-localize","title":"Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection","date":"2020-09-29","arxiv_id":"2009.14085","repositories_listed":1,"syntology":null},{"url":"/paper/self-grouping-convolutional-neural-networks","slug":"self-grouping-convolutional-neural-networks","title":"Self-grouping Convolutional Neural Networks","date":"2020-09-29","arxiv_id":"2009.13803","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-soccer-balls-with-reduced-neural","slug":"detecting-soccer-balls-with-reduced-neural","title":"Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios","date":"2020-09-28","arxiv_id":"2009.13684","repositories_listed":1,"syntology":null},{"url":"/paper/a-few-shot-learning-approach-for-historical","slug":"a-few-shot-learning-approach-for-historical","title":"A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition","date":"2020-09-26","arxiv_id":"2009.12577","repositories_listed":1,"syntology":null},{"url":"/paper/multispectral-fusion-for-object-detection","slug":"multispectral-fusion-for-object-detection","title":"Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks","date":"2020-09-26","arxiv_id":"2009.12664","repositories_listed":1,"syntology":null},{"url":"/paper/are-scene-graphs-good-enough-to-improve-image","slug":"are-scene-graphs-good-enough-to-improve-image","title":"Are scene graphs good enough to improve Image Captioning?","date":"2020-09-25","arxiv_id":"2009.12313","repositories_listed":1,"syntology":null},{"url":"/paper/tied-block-convolution-leaner-and-better-cnns","slug":"tied-block-convolution-leaner-and-better-cnns","title":"Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters","date":"2020-09-25","arxiv_id":"2009.12021","repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-efficient-post-processing-for-1","slug":"robust-and-efficient-post-processing-for-1","title":"Robust and efficient post-processing for video object detection","date":"2020-09-23","arxiv_id":"2009.11050","repositories_listed":1,"syntology":null},{"url":"/paper/grace-gradient-harmonized-and-cascaded","slug":"grace-gradient-harmonized-and-cascaded","title":"GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis","date":"2020-09-22","arxiv_id":"2009.10557","repositories_listed":1,"syntology":null},{"url":"/paper/making-images-undiscoverable-from-co-saliency","slug":"making-images-undiscoverable-from-co-saliency","title":"Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection","date":"2020-09-19","arxiv_id":"2009.09258","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/making-images-undiscoverable-from-co-saliency#ran","syntology_url":"https://syntology.ai/paper/2009.09258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09258"}},"official":{"repos":["tsingqguo/jadena"],"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/ida-improved-data-augmentation-applied-to","slug":"ida-improved-data-augmentation-applied-to","title":"IDA: Improved Data Augmentation Applied to Salient Object Detection","date":"2020-09-18","arxiv_id":"2009.08845","repositories_listed":1,"syntology":null}],"record_sha256":"e4157beacb4a7ad8113e37e8209b918136d3da557f35565853f7b4527d566424","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}