{"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/papers/34","list_of":"/task/object","task":"Object","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":34,"pages_in_order":107,"rows_per_page":100,"rows":[3301,3400],"of":10696,"counts":{"archive_papers_tagged":10696,"with_a_code_link":3979,"where_syntology_ran_a_sample":1043,"not_listed_spam_title":0,"listed":10696,"listed_where_code_ran":1043,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":919,"every_run_a_failure_of_syntologys_instrument":124,"listed_with_a_run_with_no_instrument_failure":919,"listed_every_run_a_failure_of_syntologys_instrument":124,"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","prev":"/task/object/papers/33","next":"/task/object/papers/35","papers":[{"url":"/paper/unsupervised-multi-view-cnn-for-salient-view","slug":"unsupervised-multi-view-cnn-for-salient-view","title":"Unsupervised Multi-View CNN for Salient View Selection of 3D Objects and Scenes","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/urvos-unified-referring-video-object","slug":"urvos-unified-referring-video-object","title":"URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-object-tracking-and-masking-for","slug":"dynamic-object-tracking-and-masking-for","title":"Dynamic Object Tracking and Masking for Visual SLAM","date":"2020-07-31","arxiv_id":"2008.00072","repositories_listed":1,"syntology":null},{"url":"/paper/learning-rgb-d-feature-embeddings-for-unseen","slug":"learning-rgb-d-feature-embeddings-for-unseen","title":"Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation","date":"2020-07-30","arxiv_id":"2007.15157","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-continuous-object-representation","slug":"unsupervised-continuous-object-representation","title":"Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision","date":"2020-07-30","arxiv_id":"2007.15627","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/unsupervised-continuous-object-representation#ran","syntology_url":"https://syntology.ai/paper/2007.15627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15627"}},"official":{"repos":["nicolaihaeni/corn"],"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/chained-tracker-chaining-paired-attentive","slug":"chained-tracker-chaining-paired-attentive","title":"Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking","date":"2020-07-29","arxiv_id":"2007.14557","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-3d-object-detection-from-1","slug":"weakly-supervised-3d-object-detection-from-1","title":"Weakly Supervised 3D Object Detection from Point Clouds","date":"2020-07-28","arxiv_id":"2007.13970","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/weakly-supervised-3d-object-detection-from-1#ran","syntology_url":"https://syntology.ai/paper/2007.13970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13970"}},"official":{"repos":["Zengyi-Qin/Weakly-Supervised-3D-Object-Detection"],"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/corner-proposal-network-for-anchor-free-two","slug":"corner-proposal-network-for-anchor-free-two","title":"Corner Proposal Network for Anchor-free, Two-stage Object Detection","date":"2020-07-27","arxiv_id":"2007.13816","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-bottom-up-and-top-down-attention","slug":"leveraging-bottom-up-and-top-down-attention","title":"Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection","date":"2020-07-23","arxiv_id":"2007.12104","repositories_listed":1,"syntology":null},{"url":"/paper/representation-sharing-for-fast-object","slug":"representation-sharing-for-fast-object","title":"Representation Sharing for Fast Object Detector Search and Beyond","date":"2020-07-23","arxiv_id":"2007.12075","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-3d-object-detection-from","slug":"weakly-supervised-3d-object-detection-from","title":"Weakly Supervised 3D Object Detection from Lidar Point Cloud","date":"2020-07-23","arxiv_id":"2007.11901","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/weakly-supervised-3d-object-detection-from#ran","syntology_url":"https://syntology.ai/paper/2007.11901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11901"}},"official":{"repos":["hlesmqh/WS3D"],"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/learning-the-latent-space-of-robot-dynamics","slug":"learning-the-latent-space-of-robot-dynamics","title":"Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference","date":"2020-07-22","arxiv_id":"2007.11167","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-neuromorphic-object-tracking-and","slug":"a-hybrid-neuromorphic-object-tracking-and","title":"A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems","date":"2020-07-21","arxiv_id":"2007.11404","repositories_listed":1,"syntology":null},{"url":"/paper/pillar-based-object-detection-for-autonomous","slug":"pillar-based-object-detection-for-autonomous","title":"Pillar-based Object Detection for Autonomous Driving","date":"2020-07-20","arxiv_id":"2007.10323","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/pillar-based-object-detection-for-autonomous#ran","syntology_url":"https://syntology.ai/paper/2007.10323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10323"}},"official":{"repos":["WangYueFt/pillar-od"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/geometry-constrained-weakly-supervised-object","slug":"geometry-constrained-weakly-supervised-object","title":"Geometry Constrained Weakly Supervised Object Localization","date":"2020-07-19","arxiv_id":"2007.09727","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-preserving-mask-r-cnn","slug":"boundary-preserving-mask-r-cnn","title":"Boundary-preserving Mask R-CNN","date":"2020-07-17","arxiv_id":"2007.08921","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/boundary-preserving-mask-r-cnn#ran","syntology_url":"https://syntology.ai/paper/2007.08921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08921"}},"official":{"repos":["hustvl/BMaskR-CNN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-scale-interactive-network-for-salient-1","slug":"multi-scale-interactive-network-for-salient-1","title":"Multi-scale Interactive Network for Salient Object Detection","date":"2020-07-17","arxiv_id":"2007.09062","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":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-scale-interactive-network-for-salient-1#ran","syntology_url":"https://syntology.ai/paper/2007.09062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09062"}},"official":{"repos":["lartpang/MINet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kernelized-memory-network-for-video-object","slug":"kernelized-memory-network-for-video-object","title":"Kernelized Memory Network for Video Object Segmentation","date":"2020-07-16","arxiv_id":"2007.08270","repositories_listed":1,"syntology":null},{"url":"/paper/reppoints-v2-verification-meets-regression","slug":"reppoints-v2-verification-meets-regression","title":"RepPoints V2: Verification Meets Regression for Object Detection","date":"2020-07-16","arxiv_id":"2007.08508","repositories_listed":1,"syntology":null},{"url":"/paper/unseen-object-instance-segmentation-for","slug":"unseen-object-instance-segmentation-for","title":"Unseen Object Instance Segmentation for Robotic Environments","date":"2020-07-16","arxiv_id":"2007.08073","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-weakly-supervised-object-detection","slug":"boosting-weakly-supervised-object-detection","title":"Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer","date":"2020-07-15","arxiv_id":"2007.07986","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":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) · 0 unverified","sample_list":"/paper/boosting-weakly-supervised-object-detection#ran","syntology_url":"https://syntology.ai/paper/2007.07986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07986"}},"official":{"repos":["mikuhatsune/wsod_transfer"],"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/aqd-towards-accurate-quantized-object","slug":"aqd-towards-accurate-quantized-object","title":"AQD: Towards Accurate Fully-Quantized Object Detection","date":"2020-07-14","arxiv_id":"2007.06919","repositories_listed":1,"syntology":null},{"url":"/paper/video-object-segmentation-with-episodic-graph","slug":"video-object-segmentation-with-episodic-graph","title":"Video Object Segmentation with Episodic Graph Memory Networks","date":"2020-07-14","arxiv_id":"2007.07020","repositories_listed":1,"syntology":null},{"url":"/paper/reconstruction-bottlenecks-in-object-centric","slug":"reconstruction-bottlenecks-in-object-centric","title":"Reconstruction Bottlenecks in Object-Centric Generative Models","date":"2020-07-13","arxiv_id":"2007.06245","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-self-ensembling-teacher-for-semi","slug":"temporal-self-ensembling-teacher-for-semi","title":"Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection","date":"2020-07-13","arxiv_id":"2007.06144","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-object-detection-through-an","slug":"understanding-object-detection-through-an","title":"Understanding Object Detection Through An Adversarial Lens","date":"2020-07-11","arxiv_id":"2007.05828","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-hollow-kernels-for-anatomical","slug":"learnable-hollow-kernels-for-anatomical","title":"LORCK: Learnable Object-Resembling Convolution Kernels","date":"2020-07-09","arxiv_id":"2007.05103","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-level-approach-to-waste-object","slug":"a-multi-level-approach-to-waste-object","title":"A Multi-Level Approach to Waste Object Segmentation","date":"2020-07-08","arxiv_id":"2007.04259","repositories_listed":1,"syntology":null},{"url":"/paper/detection-as-regression-certified-object","slug":"detection-as-regression-certified-object","title":"Detection as Regression: Certified Object Detection by Median Smoothing","date":"2020-07-07","arxiv_id":"2007.03730","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/detection-as-regression-certified-object#ran","syntology_url":"https://syntology.ai/paper/2007.03730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03730"}},"official":null}},{"url":"/paper/labelenc-a-new-intermediate-supervision","slug":"labelenc-a-new-intermediate-supervision","title":"LabelEnc: A New Intermediate Supervision Method for Object Detection","date":"2020-07-07","arxiv_id":"2007.03282","repositories_listed":1,"syntology":null},{"url":"/paper/single-shot-video-object-detector","slug":"single-shot-video-object-detector","title":"Single Shot Video Object Detector","date":"2020-07-07","arxiv_id":"2007.03560","repositories_listed":1,"syntology":null},{"url":"/paper/point-set-anchors-for-object-detection","slug":"point-set-anchors-for-object-detection","title":"Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation","date":"2020-07-06","arxiv_id":"2007.02846","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-distances-for-stereo-disparity","slug":"wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","arxiv_id":"2007.03085","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wasserstein-distances-for-stereo-disparity#ran","syntology_url":"https://syntology.ai/paper/2007.03085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03085"}},"official":{"repos":["Div99/W-Stereo-Disp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-based-joint-detection-of-object-and","slug":"attention-based-joint-detection-of-object-and","title":"Attention-based Joint Detection of Object and Semantic Part","date":"2020-07-05","arxiv_id":"2007.02419","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/attention-based-joint-detection-of-object-and#ran","syntology_url":"https://syntology.ai/paper/2007.02419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02419"}},"official":{"repos":["kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-weakly-supervised-visual-grounding","slug":"improving-weakly-supervised-visual-grounding","title":"Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation","date":"2020-07-03","arxiv_id":"2007.01951","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/improving-weakly-supervised-visual-grounding#ran","syntology_url":"https://syntology.ai/paper/2007.01951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01951"}},"official":{"repos":["jhuang81/weak-sup-visual-grounding"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pointtrack-for-effective-online-multi-object","slug":"pointtrack-for-effective-online-multi-object","title":"PointTrack++ for Effective Online Multi-Object Tracking and Segmentation","date":"2020-07-03","arxiv_id":"2007.01549","repositories_listed":1,"syntology":null},{"url":"/paper/are-there-any-object-detectors-in-the-hidden-1","slug":"are-there-any-object-detectors-in-the-hidden-1","title":"Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?","date":"2020-07-02","arxiv_id":"2007.01062","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/are-there-any-object-detectors-in-the-hidden-1#ran","syntology_url":"https://syntology.ai/paper/2007.01062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01062"}},"official":{"repos":["ellagale/testing_object_detectors_in_deepCNNs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-orientation-distributions-for-object","slug":"learning-orientation-distributions-for-object","title":"Learning Orientation Distributions for Object Pose Estimation","date":"2020-07-02","arxiv_id":"2007.01418","repositories_listed":1,"syntology":null},{"url":"/paper/relate-physically-plausible-multi-object","slug":"relate-physically-plausible-multi-object","title":"RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces","date":"2020-07-02","arxiv_id":"2007.01272","repositories_listed":1,"syntology":null},{"url":"/paper/learning-geocentric-object-pose-in-oblique-1","slug":"learning-geocentric-object-pose-in-oblique-1","title":"Learning Geocentric Object Pose in Oblique Monocular Images","date":"2020-07-01","arxiv_id":"2007.00729","repositories_listed":1,"syntology":null},{"url":"/paper/the-ikea-asm-dataset-understanding-people","slug":"the-ikea-asm-dataset-understanding-people","title":"The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose","date":"2020-07-01","arxiv_id":"2007.00394","repositories_listed":1,"syntology":null},{"url":"/paper/monet3d-towards-accurate-monocular-3d-object","slug":"monet3d-towards-accurate-monocular-3d-object","title":"MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time","date":"2020-06-29","arxiv_id":"2006.16007","repositories_listed":1,"syntology":null},{"url":"/paper/predictive-and-generative-neural-networks-for","slug":"predictive-and-generative-neural-networks-for","title":"Predictive and Generative Neural Networks for Object Functionality","date":"2020-06-28","arxiv_id":"2006.15520","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-discovery-of-object-landmarks","slug":"unsupervised-discovery-of-object-landmarks","title":"On Equivariant and Invariant Learning of Object Landmark Representations","date":"2020-06-26","arxiv_id":"2006.14787","repositories_listed":1,"syntology":null},{"url":"/paper/can-3d-adversarial-logos-cloak-humans","slug":"can-3d-adversarial-logos-cloak-humans","title":"Can 3D Adversarial Logos Cloak Humans?","date":"2020-06-25","arxiv_id":"2006.14655","repositories_listed":1,"syntology":null},{"url":"/paper/lifted-disjoint-paths-with-application-in-1","slug":"lifted-disjoint-paths-with-application-in-1","title":"Lifted Disjoint Paths with Application in Multiple Object Tracking","date":"2020-06-25","arxiv_id":"2006.14550","repositories_listed":1,"syntology":null},{"url":"/paper/development-and-evaluation-of-a-test-setup-to","slug":"development-and-evaluation-of-a-test-setup-to","title":"Development and evaluation of a test setup to investigate distance differences in immersive virtual environments","date":"2020-06-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/iffdetector-inference-aware-feature-filtering","slug":"iffdetector-inference-aware-feature-filtering","title":"iffDetector: Inference-aware Feature Filtering for Object Detection","date":"2020-06-23","arxiv_id":"2006.12708","repositories_listed":1,"syntology":null},{"url":"/paper/joint-detection-and-multi-object-tracking","slug":"joint-detection-and-multi-object-tracking","title":"Joint Object Detection and Multi-Object Tracking with Graph Neural Networks","date":"2020-06-23","arxiv_id":"2006.13164","repositories_listed":1,"syntology":null},{"url":"/paper/robot-object-retrieval-with-contextual","slug":"robot-object-retrieval-with-contextual","title":"Robot Object Retrieval with Contextual Natural Language Queries","date":"2020-06-23","arxiv_id":"2006.13253","repositories_listed":1,"syntology":null},{"url":"/paper/learning-physical-graph-representations-from","slug":"learning-physical-graph-representations-from","title":"Learning Physical Graph Representations from Visual Scenes","date":"2020-06-22","arxiv_id":"2006.12373","repositories_listed":1,"syntology":null},{"url":"/paper/video-moment-localization-using-object","slug":"video-moment-localization-using-object","title":"Video Moment Localization using Object Evidence and Reverse Captioning","date":"2020-06-18","arxiv_id":"2006.10260","repositories_listed":1,"syntology":null},{"url":"/paper/explanation-based-weakly-supervised-learning","slug":"explanation-based-weakly-supervised-learning","title":"Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks","date":"2020-06-16","arxiv_id":"2006.09562","repositories_listed":1,"syntology":null},{"url":"/paper/hyperflow-representing-3d-objects-as-surfaces","slug":"hyperflow-representing-3d-objects-as-surfaces","title":"HyperFlow: Representing 3D Objects as Surfaces","date":"2020-06-15","arxiv_id":"2006.08710","repositories_listed":1,"syntology":null},{"url":"/paper/fcos-a-simple-and-strong-anchor-free-object","slug":"fcos-a-simple-and-strong-anchor-free-object","title":"FCOS: A simple and strong anchor-free object detector","date":"2020-06-14","arxiv_id":"2006.09214","repositories_listed":1,"syntology":null},{"url":"/paper/gnn3dmot-graph-neural-network-for-3d-multi-1","slug":"gnn3dmot-graph-neural-network-for-3d-multi-1","title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning","date":"2020-06-12","arxiv_id":"2006.07327","repositories_listed":1,"syntology":null},{"url":"/paper/unmasking-the-inductive-biases-of","slug":"unmasking-the-inductive-biases-of","title":"Benchmarking Unsupervised Object Representations for Video Sequences","date":"2020-06-12","arxiv_id":"2006.07034","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unmasking-the-inductive-biases-of#ran","syntology_url":"https://syntology.ai/paper/2006.07034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07034"}},"official":{"repos":["ecker-lab/object-centric-representation-benchmark"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-a-unified-sample-weighting-network-1","slug":"learning-a-unified-sample-weighting-network-1","title":"Learning a Unified Sample Weighting Network for Object Detection","date":"2020-06-11","arxiv_id":"2006.06568","repositories_listed":1,"syntology":null},{"url":"/paper/condensing-two-stage-detection-with-automatic","slug":"condensing-two-stage-detection-with-automatic","title":"Condensing Two-stage Detection with Automatic Object Key Part Discovery","date":"2020-06-10","arxiv_id":"2006.05597","repositories_listed":1,"syntology":null},{"url":"/paper/tubetk-adopting-tubes-to-track-multi-object-1","slug":"tubetk-adopting-tubes-to-track-multi-object-1","title":"TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model","date":"2020-06-10","arxiv_id":"2006.05683","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/tubetk-adopting-tubes-to-track-multi-object-1#ran","syntology_url":"https://syntology.ai/paper/2006.05683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05683"}},"official":{"repos":["BoPang1996/TubeTK"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-localization-map-towards-accurate","slug":"rethinking-localization-map-towards-accurate","title":"Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps","date":"2020-06-09","arxiv_id":"2006.05220","repositories_listed":1,"syntology":null},{"url":"/paper/rgb-d-e-event-camera-calibration-for-fast-6","slug":"rgb-d-e-event-camera-calibration-for-fast-6","title":"RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking","date":"2020-06-09","arxiv_id":"2006.05011","repositories_listed":1,"syntology":null},{"url":"/paper/big-gans-are-watching-you-towards","slug":"big-gans-are-watching-you-towards","title":"Object Segmentation Without Labels with Large-Scale Generative Models","date":"2020-06-08","arxiv_id":"2006.04988","repositories_listed":1,"syntology":null},{"url":"/paper/siamese-keypoint-prediction-network-for","slug":"siamese-keypoint-prediction-network-for","title":"Siamese Keypoint Prediction Network for Visual Object Tracking","date":"2020-06-07","arxiv_id":"2006.04078","repositories_listed":1,"syntology":null},{"url":"/paper/novel-object-viewpoint-estimation-through-1","slug":"novel-object-viewpoint-estimation-through-1","title":"Novel Object Viewpoint Estimation through Reconstruction Alignment","date":"2020-06-05","arxiv_id":"2006.03586","repositories_listed":1,"syntology":null},{"url":"/paper/pick-object-attack-type-specific-adversarial","slug":"pick-object-attack-type-specific-adversarial","title":"Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection","date":"2020-06-05","arxiv_id":"2006.03184","repositories_listed":1,"syntology":null},{"url":"/paper/circlenet-anchor-free-detection-with-circle","slug":"circlenet-anchor-free-detection-with-circle","title":"CircleNet: Anchor-free Detection with Circle Representation","date":"2020-06-03","arxiv_id":"2006.02474","repositories_listed":1,"syntology":null},{"url":"/paper/interpolation-based-semi-supervised-learning","slug":"interpolation-based-semi-supervised-learning","title":"Interpolation-based semi-supervised learning for object detection","date":"2020-06-03","arxiv_id":"2006.02158","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/interpolation-based-semi-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2006.02158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02158"}},"official":null}},{"url":"/paper/object-independent-human-to-robot-handovers","slug":"object-independent-human-to-robot-handovers","title":"Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision","date":"2020-06-02","arxiv_id":"2006.01797","repositories_listed":1,"syntology":null},{"url":"/paper/3d-mpa-multi-proposal-aggregation-for-3d-1","slug":"3d-mpa-multi-proposal-aggregation-for-3d-1","title":"3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/d2det-towards-high-quality-object-detection","slug":"d2det-towards-high-quality-object-detection","title":"D2Det: Towards High Quality Object Detection and Instance Segmentation","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/discovering-human-interactions-with-novel","slug":"discovering-human-interactions-with-novel","title":"Discovering Human Interactions With Novel Objects via Zero-Shot Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dunit-detection-based-unsupervised-image-to","slug":"dunit-detection-based-unsupervised-image-to","title":"DUNIT: Detection-Based Unsupervised Image-to-Image Translation","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ganhand-predicting-human-grasp-affordances-in","slug":"ganhand-predicting-human-grasp-affordances-in","title":"GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gnn3dmot-graph-neural-network-for-3d-multi","slug":"gnn3dmot-graph-neural-network-for-3d-multi","title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ida-3d-instance-depth-aware-3d-object","slug":"ida-3d-instance-depth-aware-3d-object","title":"IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous Driving","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-neural-solver-for-multiple-object-1","slug":"learning-a-neural-solver-for-multiple-object-1","title":"Learning a Neural Solver for Multiple Object Tracking","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sparse-layered-graphs-for-multi-object","slug":"sparse-layered-graphs-for-multi-object","title":"Sparse Layered Graphs for Multi-Object Segmentation","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/taking-a-deeper-look-at-co-salient-object","slug":"taking-a-deeper-look-at-co-salient-object","title":"Taking a Deeper Look at Co-Salient Object Detection","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/where-does-it-end-reasoning-about-hidden-1","slug":"where-does-it-end-reasoning-about-hidden-1","title":"Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/is-depth-really-necessary-for-salient-object","slug":"is-depth-really-necessary-for-salient-object","title":"Is Depth Really Necessary for Salient Object Detection?","date":"2020-05-30","arxiv_id":"2006.00269","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-and-efficient-heterogeneous","slug":"interpretable-and-efficient-heterogeneous","title":"Interpretable and Efficient Heterogeneous Graph Convolutional Network","date":"2020-05-27","arxiv_id":"2005.13183","repositories_listed":1,"syntology":null},{"url":"/paper/alba-reinforcement-learning-for-video-object","slug":"alba-reinforcement-learning-for-video-object","title":"ALBA : Reinforcement Learning for Video Object Segmentation","date":"2020-05-26","arxiv_id":"2005.13039","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-and-accurate-fine-grained","slug":"interpretable-and-accurate-fine-grained","title":"Interpretable and Accurate Fine-grained Recognition via Region Grouping","date":"2020-05-21","arxiv_id":"2005.10411","repositories_listed":1,"syntology":null},{"url":"/paper/train-in-germany-test-in-the-usa-making-3d","slug":"train-in-germany-test-in-the-usa-making-3d","title":"Train in Germany, Test in The USA: Making 3D Object Detectors Generalize","date":"2020-05-17","arxiv_id":"2005.08139","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"7 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/train-in-germany-test-in-the-usa-making-3d#ran","syntology_url":"https://syntology.ai/paper/2005.08139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08139"}},"official":{"repos":["cxy1997/3D_adapt_auto_driving"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/one-shot-object-detection-without-fine-tuning","slug":"one-shot-object-detection-without-fine-tuning","title":"One-Shot Object Detection without Fine-Tuning","date":"2020-05-08","arxiv_id":"2005.03819","repositories_listed":1,"syntology":null},{"url":"/paper/a-hand-motion-guided-articulation-and","slug":"a-hand-motion-guided-articulation-and","title":"A Hand Motion-guided Articulation and Segmentation Estimation","date":"2020-05-07","arxiv_id":"2005.03691","repositories_listed":1,"syntology":null},{"url":"/paper/reproduction-of-lateral-inhibition-inspired","slug":"reproduction-of-lateral-inhibition-inspired","title":"Reproduction of Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection","date":"2020-05-05","arxiv_id":"2005.02184","repositories_listed":1,"syntology":null},{"url":"/paper/drotrack-high-speed-drone-based-object","slug":"drotrack-high-speed-drone-based-object","title":"DroTrack: High-speed Drone-based Object Tracking Under Uncertainty","date":"2020-05-02","arxiv_id":"2005.00828","repositories_listed":1,"syntology":null},{"url":"/paper/bilateral-attention-network-for-rgb-d-salient","slug":"bilateral-attention-network-for-rgb-d-salient","title":"Bilateral Attention Network for RGB-D Salient Object Detection","date":"2020-04-30","arxiv_id":"2004.14582","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-language-binding-in-relational-visual","slug":"dynamic-language-binding-in-relational-visual","title":"Dynamic Language Binding in Relational Visual Reasoning","date":"2020-04-30","arxiv_id":"2004.14603","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":5,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dynamic-language-binding-in-relational-visual#ran","syntology_url":"https://syntology.ai/paper/2004.14603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14603"}},"official":null}},{"url":"/paper/multivariate-confidence-calibration-for","slug":"multivariate-confidence-calibration-for","title":"Multivariate Confidence Calibration for Object Detection","date":"2020-04-28","arxiv_id":"2004.13546","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/multivariate-confidence-calibration-for#ran","syntology_url":"https://syntology.ai/paper/2004.13546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13546"}},"official":{"repos":["fabiankueppers/calibration-framework"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cnn-based-road-user-detection-using-the-3d","slug":"cnn-based-road-user-detection-using-the-3d","title":"CNN based Road User Detection using the 3D Radar Cube","date":"2020-04-25","arxiv_id":"2004.12165","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-sequence-to-sequence-video-object","slug":"revisiting-sequence-to-sequence-video-object","title":"Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory","date":"2020-04-25","arxiv_id":"2004.12170","repositories_listed":1,"syntology":null},{"url":"/paper/dpdist-comparing-point-clouds-using-deep","slug":"dpdist-comparing-point-clouds-using-deep","title":"DPDist : Comparing Point Clouds Using Deep Point Cloud Distance","date":"2020-04-24","arxiv_id":"2004.11784","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-knowledge-from-refinement-in","slug":"distilling-knowledge-from-refinement-in","title":"Distilling Knowledge from Refinement in Multiple Instance Detection Networks","date":"2020-04-23","arxiv_id":"2004.10943","repositories_listed":1,"syntology":null},{"url":"/paper/continual-learning-of-object-instances","slug":"continual-learning-of-object-instances","title":"Continual Learning of Object Instances","date":"2020-04-22","arxiv_id":"2004.10862","repositories_listed":1,"syntology":null},{"url":"/paper/a-baseline-for-the-commands-for-autonomous","slug":"a-baseline-for-the-commands-for-autonomous","title":"A Baseline for the Commands For Autonomous Vehicles Challenge","date":"2020-04-20","arxiv_id":"2004.13822","repositories_listed":1,"syntology":null},{"url":"/paper/detailed-2d-3d-joint-representation-for-human","slug":"detailed-2d-3d-joint-representation-for-human","title":"Detailed 2D-3D Joint Representation for Human-Object Interaction","date":"2020-04-17","arxiv_id":"2004.08154","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/detailed-2d-3d-joint-representation-for-human#ran","syntology_url":"https://syntology.ai/paper/2004.08154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08154"}},"official":{"repos":["DirtyHarryLYL/DJ-RN"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-transductive-approach-for-video-object","slug":"a-transductive-approach-for-video-object","title":"A Transductive Approach for Video Object Segmentation","date":"2020-04-15","arxiv_id":"2004.07193","repositories_listed":1,"syntology":null}],"record_sha256":"10f9756861bab26c4fdaa4973beec5ed95d395525f0a2cfde77dbc6081cae8fd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}