{"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/27","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":27,"pages_in_order":107,"rows_per_page":100,"rows":[2601,2700],"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/26","next":"/task/object/papers/28","papers":[{"url":"/paper/task-specific-attention-is-one-more-thing-you","slug":"task-specific-attention-is-one-more-thing-you","title":"Task Specific Attention is one more thing you need for object detection","date":"2022-02-18","arxiv_id":"2202.09048","repositories_listed":1,"syntology":null},{"url":"/paper/carl-d-a-vision-benchmark-suite-and-large","slug":"carl-d-a-vision-benchmark-suite-and-large","title":"CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation","date":"2022-02-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/developing-imperceptible-adversarial-patches","slug":"developing-imperceptible-adversarial-patches","title":"Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies","date":"2022-02-17","arxiv_id":"2202.08892","repositories_listed":1,"syntology":null},{"url":"/paper/domain-randomization-for-object-counting","slug":"domain-randomization-for-object-counting","title":"Domain Randomization for Object Counting","date":"2022-02-17","arxiv_id":"2202.08670","repositories_listed":1,"syntology":null},{"url":"/paper/vision-models-are-more-robust-and-fair-when","slug":"vision-models-are-more-robust-and-fair-when","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","date":"2022-02-16","arxiv_id":"2202.08360","repositories_listed":1,"syntology":null},{"url":"/paper/sodar-segmenting-objects-by","slug":"sodar-segmenting-objects-by","title":"SODAR: Segmenting Objects by DynamicallyAggregating Neighboring Mask Representations","date":"2022-02-15","arxiv_id":"2202.07402","repositories_listed":1,"syntology":null},{"url":"/paper/box-supervised-video-segmentation-proposal","slug":"box-supervised-video-segmentation-proposal","title":"Box Supervised Video Segmentation Proposal Network","date":"2022-02-14","arxiv_id":"2202.07025","repositories_listed":1,"syntology":null},{"url":"/paper/clipasso-semantically-aware-object-sketching","slug":"clipasso-semantically-aware-object-sketching","title":"CLIPasso: Semantically-Aware Object Sketching","date":"2022-02-11","arxiv_id":"2202.05822","repositories_listed":1,"syntology":null},{"url":"/paper/safepicking-learning-safe-object-extraction","slug":"safepicking-learning-safe-object-extraction","title":"SafePicking: Learning Safe Object Extraction via Object-Level Mapping","date":"2022-02-11","arxiv_id":"2202.05832","repositories_listed":1,"syntology":null},{"url":"/paper/tiny-object-tracking-a-large-scale-dataset","slug":"tiny-object-tracking-a-large-scale-dataset","title":"Tiny Object Tracking: A Large-scale Dataset and A Baseline","date":"2022-02-11","arxiv_id":"2202.05659","repositories_listed":1,"syntology":null},{"url":"/paper/factored-world-models-for-zero-shot-1","slug":"factored-world-models-for-zero-shot-1","title":"Factored World Models for Zero-Shot Generalization in Robotic Manipulation","date":"2022-02-10","arxiv_id":"2202.05333","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-siamese-multiple-object-tracker","slug":"real-time-siamese-multiple-object-tracker","title":"Real-Time Siamese Multiple Object Tracker with Enhanced Proposals","date":"2022-02-10","arxiv_id":"2202.04966","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-deep-models-for-salient-object","slug":"benchmarking-deep-models-for-salient-object","title":"Benchmarking Deep Models for Salient Object Detection","date":"2022-02-07","arxiv_id":"2202.02925","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-based-boundary-aware-network-for","slug":"scribble-based-boundary-aware-network-for","title":"Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images","date":"2022-02-07","arxiv_id":"2202.03501","repositories_listed":1,"syntology":null},{"url":"/paper/on-smart-gaze-based-annotation-of","slug":"on-smart-gaze-based-annotation-of","title":"On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks","date":"2022-02-06","arxiv_id":"2202.02764","repositories_listed":1,"syntology":null},{"url":"/paper/objectseeker-certifiably-robust-object","slug":"objectseeker-certifiably-robust-object","title":"ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking","date":"2022-02-03","arxiv_id":"2202.01811","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"9 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/objectseeker-certifiably-robust-object#ran","syntology_url":"https://syntology.ai/paper/2202.01811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.01811"}},"official":{"repos":["inspire-group/ObjectSeeker"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/detecting-human-object-interactions-with-2","slug":"detecting-human-object-interactions-with-2","title":"Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics","date":"2022-02-01","arxiv_id":"2202.00259","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/detecting-human-object-interactions-with-2#ran","syntology_url":"https://syntology.ai/paper/2202.00259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.00259"}},"official":{"repos":["jacobyuan7/ocn-hoi-benchmark"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/interactron-embodied-adaptive-object","slug":"interactron-embodied-adaptive-object","title":"Interactron: Embodied Adaptive Object Detection","date":"2022-02-01","arxiv_id":"2202.00660","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 3 unverified","sample_list":"/paper/interactron-embodied-adaptive-object#ran","syntology_url":"https://syntology.ai/paper/2202.00660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.00660"}},"official":{"repos":["allenai/interactron"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/few-shot-backdoor-attacks-on-visual-object-1","slug":"few-shot-backdoor-attacks-on-visual-object-1","title":"Few-Shot Backdoor Attacks on Visual Object Tracking","date":"2022-01-31","arxiv_id":"2201.13178","repositories_listed":1,"syntology":null},{"url":"/paper/a-tomographic-workflow-to-enable-deep","slug":"a-tomographic-workflow-to-enable-deep","title":"A tomographic workflow to enable deep learning for X-ray based foreign object detection","date":"2022-01-28","arxiv_id":"2201.12184","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-optimization-of-metasurfaces-for","slug":"end-to-end-optimization-of-metasurfaces-for","title":"End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing","date":"2022-01-28","arxiv_id":"2201.12348","repositories_listed":1,"syntology":null},{"url":"/paper/reltr-relation-transformer-for-scene-graph","slug":"reltr-relation-transformer-for-scene-graph","title":"RelTR: Relation Transformer for Scene Graph Generation","date":"2022-01-27","arxiv_id":"2201.11460","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-the-mutual-error-amplification-for","slug":"mitigating-the-mutual-error-amplification-for","title":"CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection","date":"2022-01-26","arxiv_id":"2201.10734","repositories_listed":1,"syntology":null},{"url":"/paper/monodistill-learning-spatial-features-for-1","slug":"monodistill-learning-spatial-features-for-1","title":"MonoDistill: Learning Spatial Features for Monocular 3D Object Detection","date":"2022-01-26","arxiv_id":"2201.10830","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-fusion-strategies-for-accurate-rgbt","slug":"exploring-fusion-strategies-for-accurate-rgbt","title":"Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking","date":"2022-01-21","arxiv_id":"2201.08673","repositories_listed":1,"syntology":null},{"url":"/paper/high-fidelity-3d-model-compression-based-on","slug":"high-fidelity-3d-model-compression-based-on","title":"High-fidelity 3D Model Compression based on Key Spheres","date":"2022-01-19","arxiv_id":"2201.07486","repositories_listed":1,"syntology":null},{"url":"/paper/semi-automatic-3d-object-keypoint-annotation","slug":"semi-automatic-3d-object-keypoint-annotation","title":"Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses","date":"2022-01-19","arxiv_id":"2201.07665","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-proposals-refinement-for-3d","slug":"attention-based-proposals-refinement-for-3d","title":"Attention-based Proposals Refinement for 3D Object Detection","date":"2022-01-18","arxiv_id":"2201.07070","repositories_listed":1,"syntology":null},{"url":"/paper/equalized-focal-loss-for-dense-long-tailed","slug":"equalized-focal-loss-for-dense-long-tailed","title":"Equalized Focal Loss for Dense Long-Tailed Object Detection","date":"2022-01-07","arxiv_id":"2201.02593","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/equalized-focal-loss-for-dense-long-tailed#ran","syntology_url":"https://syntology.ai/paper/2201.02593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.02593"}},"official":{"repos":["modeltc/eod"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neroic-neural-rendering-of-objects-from","slug":"neroic-neural-rendering-of-objects-from","title":"NeROIC: Neural Rendering of Objects from Online Image Collections","date":"2022-01-07","arxiv_id":"2201.02533","repositories_listed":1,"syntology":null},{"url":"/paper/to-miss-attend-is-to-misalign-residual-self","slug":"to-miss-attend-is-to-misalign-residual-self","title":"To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors","date":"2022-01-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-realistic-symmetry-based-completion","slug":"towards-realistic-symmetry-based-completion","title":"Towards realistic symmetry-based completion of previously unseen point clouds","date":"2022-01-05","arxiv_id":"2201.01858","repositories_listed":1,"syntology":null},{"url":"/paper/language-as-queries-for-referring-video","slug":"language-as-queries-for-referring-video","title":"Language as Queries for Referring Video Object Segmentation","date":"2022-01-03","arxiv_id":"2201.00487","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/language-as-queries-for-referring-video#ran","syntology_url":"https://syntology.ai/paper/2201.00487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00487"}},"official":{"repos":["wjn922/referformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-open-world-object-detection","slug":"revisiting-open-world-object-detection","title":"Revisiting Open World Object Detection","date":"2022-01-03","arxiv_id":"2201.00471","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/revisiting-open-world-object-detection#ran","syntology_url":"https://syntology.ai/paper/2201.00471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00471"}},"official":{"repos":["re-owod/re-owod"],"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/c2am-contrastive-learning-of-class-agnostic","slug":"c2am-contrastive-learning-of-class-agnostic","title":"C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detecting-camouflaged-object-in-frequency","slug":"detecting-camouflaged-object-in-frequency","title":"Detecting Camouflaged Object in Frequency Domain","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detectordetective-investigating-the-effects","slug":"detectordetective-investigating-the-effects","title":"DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/geometric-and-textural-augmentation-for","slug":"geometric-and-textural-augmentation-for","title":"Geometric and Textural Augmentation for Domain Gap Reduction","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/posetrack21-a-dataset-for-person-search-multi","slug":"posetrack21-a-dataset-for-person-search-multi","title":"PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-region-feature-synthesizer-for-zero","slug":"robust-region-feature-synthesizer-for-zero","title":"Robust Region Feature Synthesizer for Zero-Shot Object Detection","date":"2022-01-01","arxiv_id":"2201.00103","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":2,"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/robust-region-feature-synthesizer-for-zero#ran","syntology_url":"https://syntology.ai/paper/2201.00103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00103"}},"official":{"repos":["HPL123/RRFS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/single-domain-generalized-object-detection-in","slug":"single-domain-generalized-object-detection-in","title":"Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sporeagent-reinforced-scene-level","slug":"sporeagent-reinforced-scene-level","title":"SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement","date":"2022-01-01","arxiv_id":"2201.00239","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-rotation-invariant-aerial","slug":"weakly-supervised-rotation-invariant-aerial","title":"Weakly Supervised Rotation-Invariant Aerial Object Detection Network","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/icaps-iterative-category-level-object-pose","slug":"icaps-iterative-category-level-object-pose","title":"iCaps: Iterative Category-level Object Pose and Shape Estimation","date":"2021-12-31","arxiv_id":"2201.00059","repositories_listed":1,"syntology":null},{"url":"/paper/background-aware-classification-activation","slug":"background-aware-classification-activation","title":"Background-aware Classification Activation Map for Weakly Supervised Object Localization","date":"2021-12-29","arxiv_id":"2112.14379","repositories_listed":1,"syntology":null},{"url":"/paper/metagraspnet-a-large-scale-benchmark-dataset","slug":"metagraspnet-a-large-scale-benchmark-dataset","title":"MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis","date":"2021-12-29","arxiv_id":"2112.14663","repositories_listed":1,"syntology":null},{"url":"/paper/siamese-network-with-interactive-transformer","slug":"siamese-network-with-interactive-transformer","title":"Siamese Network with Interactive Transformer for Video Object Segmentation","date":"2021-12-28","arxiv_id":"2112.13983","repositories_listed":1,"syntology":null},{"url":"/paper/miti-detr-object-detection-based-on","slug":"miti-detr-object-detection-based-on","title":"Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence","date":"2021-12-26","arxiv_id":"2112.13310","repositories_listed":1,"syntology":null},{"url":"/paper/class-aware-sounding-objects-localization-via","slug":"class-aware-sounding-objects-localization-via","title":"Class-aware Sounding Objects Localization via Audiovisual Correspondence","date":"2021-12-22","arxiv_id":"2112.11749","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":3,"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/class-aware-sounding-objects-localization-via#ran","syntology_url":"https://syntology.ai/paper/2112.11749","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11749"}},"official":{"repos":["gewu-lab/csol_tpami2021"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/leveraging-synthetic-data-in-object-detection","slug":"leveraging-synthetic-data-in-object-detection","title":"Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles","date":"2021-12-22","arxiv_id":"2112.12252","repositories_listed":1,"syntology":null},{"url":"/paper/goal-generating-4d-whole-body-motion-for-hand","slug":"goal-generating-4d-whole-body-motion-for-hand","title":"GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping","date":"2021-12-21","arxiv_id":"2112.11454","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/goal-generating-4d-whole-body-motion-for-hand#ran","syntology_url":"https://syntology.ai/paper/2112.11454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11454"}},"official":{"repos":["otaheri/GOAL"],"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/watch-it-move-unsupervised-discovery-of-3d","slug":"watch-it-move-unsupervised-discovery-of-3d","title":"Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects","date":"2021-12-21","arxiv_id":"2112.11347","repositories_listed":1,"syntology":null},{"url":"/paper/scanqa-3d-question-answering-for-spatial","slug":"scanqa-3d-question-answering-for-spatial","title":"ScanQA: 3D Question Answering for Spatial Scene Understanding","date":"2021-12-20","arxiv_id":"2112.10482","repositories_listed":1,"syntology":null},{"url":"/paper/saga-stochastic-whole-body-grasping-with","slug":"saga-stochastic-whole-body-grasping-with","title":"SAGA: Stochastic Whole-Body Grasping with Contact","date":"2021-12-19","arxiv_id":"2112.10103","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/saga-stochastic-whole-body-grasping-with#ran","syntology_url":"https://syntology.ai/paper/2112.10103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.10103"}},"official":null}},{"url":"/paper/exploiting-long-term-dependencies-for","slug":"exploiting-long-term-dependencies-for","title":"Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs","date":"2021-12-18","arxiv_id":"2112.09828","repositories_listed":1,"syntology":null},{"url":"/paper/online-grounding-of-pddl-domains-by-acting","slug":"online-grounding-of-pddl-domains-by-acting","title":"Online Grounding of Symbolic Planning Domains in Unknown Environments","date":"2021-12-18","arxiv_id":"2112.10007","repositories_listed":1,"syntology":null},{"url":"/paper/dprost-6-dof-object-pose-estimation-using","slug":"dprost-6-dof-object-pose-estimation-using","title":"DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation","date":"2021-12-16","arxiv_id":"2112.08775","repositories_listed":1,"syntology":null},{"url":"/paper/hodor-high-level-object-descriptors-for","slug":"hodor-high-level-object-descriptors-for","title":"HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images","date":"2021-12-16","arxiv_id":"2112.09131","repositories_listed":1,"syntology":null},{"url":"/paper/human-hands-as-probes-for-interactive-object","slug":"human-hands-as-probes-for-interactive-object","title":"Human Hands as Probes for Interactive Object Understanding","date":"2021-12-16","arxiv_id":"2112.09120","repositories_listed":1,"syntology":null},{"url":"/paper/is-count-large-scale-object-counting-from","slug":"is-count-large-scale-object-counting-from","title":"IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling","date":"2021-12-16","arxiv_id":"2112.09126","repositories_listed":1,"syntology":null},{"url":"/paper/looking-outside-the-box-to-ground-language-in","slug":"looking-outside-the-box-to-ground-language-in","title":"Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds","date":"2021-12-16","arxiv_id":"2112.08879","repositories_listed":1,"syntology":null},{"url":"/paper/qahoi-query-based-anchors-for-human-object","slug":"qahoi-query-based-anchors-for-human-object","title":"QAHOI: Query-Based Anchors for Human-Object Interaction Detection","date":"2021-12-16","arxiv_id":"2112.08647","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/qahoi-query-based-anchors-for-human-object#ran","syntology_url":"https://syntology.ai/paper/2112.08647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.08647"}},"official":{"repos":["cjw2021/QAHOI"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/regionclip-region-based-language-image","slug":"regionclip-region-based-language-image","title":"RegionCLIP: Region-based Language-Image Pretraining","date":"2021-12-16","arxiv_id":"2112.09106","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-object-states-vs-detecting-objects","slug":"detecting-object-states-vs-detecting-objects","title":"Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study","date":"2021-12-15","arxiv_id":"2112.08281","repositories_listed":1,"syntology":null},{"url":"/paper/feature-attending-recurrent-modules-for","slug":"feature-attending-recurrent-modules-for","title":"Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning","date":"2021-12-15","arxiv_id":"2112.08369","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-multi-object-tracking-in-the","slug":"reliable-multi-object-tracking-in-the","title":"Reliable Multi-Object Tracking in the Presence of Unreliable Detections","date":"2021-12-15","arxiv_id":"2112.08345","repositories_listed":1,"syntology":null},{"url":"/paper/tracer-extreme-attention-guided-salient","slug":"tracer-extreme-attention-guided-salient","title":"TRACER: Extreme Attention Guided Salient Object Tracing Network","date":"2021-12-14","arxiv_id":"2112.07380","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tracer-extreme-attention-guided-salient#ran","syntology_url":"https://syntology.ai/paper/2112.07380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.07380"}},"official":{"repos":["Karel911/TRACER"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/anchor-retouching-via-model-interaction-for","slug":"anchor-retouching-via-model-interaction-for","title":"Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images","date":"2021-12-13","arxiv_id":"2112.06701","repositories_listed":1,"syntology":null},{"url":"/paper/sac-gan-structure-aware-image-to-image","slug":"sac-gan-structure-aware-image-to-image","title":"SAC-GAN: Structure-Aware Image Composition","date":"2021-12-13","arxiv_id":"2112.06596","repositories_listed":1,"syntology":null},{"url":"/paper/visual-transformers-with-primal-object","slug":"visual-transformers-with-primal-object","title":"Visual Transformers with Primal Object Queries for Multi-Label Image Classification","date":"2021-12-10","arxiv_id":"2112.05485","repositories_listed":1,"syntology":null},{"url":"/paper/dual-cluster-contrastive-learning-for-person","slug":"dual-cluster-contrastive-learning-for-person","title":"Dual Cluster Contrastive learning for Object Re-Identification","date":"2021-12-09","arxiv_id":"2112.04662","repositories_listed":1,"syntology":null},{"url":"/paper/neural-descriptor-fields-se-3-equivariant","slug":"neural-descriptor-fields-se-3-equivariant","title":"Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation","date":"2021-12-09","arxiv_id":"2112.05124","repositories_listed":1,"syntology":null},{"url":"/paper/searching-parameterized-ap-loss-for-object-1","slug":"searching-parameterized-ap-loss-for-object-1","title":"Searching Parameterized AP Loss for Object Detection","date":"2021-12-09","arxiv_id":"2112.05138","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":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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/searching-parameterized-ap-loss-for-object-1#ran","syntology_url":"https://syntology.ai/paper/2112.05138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.05138"}},"official":{"repos":["fundamentalvision/parameterized-ap-loss"],"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/object-shape-error-response-using-bayesian-3","slug":"object-shape-error-response-using-bayesian-3","title":"Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts","date":"2021-12-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/segment-and-complete-defending-object","slug":"segment-and-complete-defending-object","title":"Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection","date":"2021-12-08","arxiv_id":"2112.04532","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/segment-and-complete-defending-object#ran","syntology_url":"https://syntology.ai/paper/2112.04532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.04532"}},"official":{"repos":["joellliu/segmentandcomplete"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sok-vehicle-orientation-representations-for","slug":"sok-vehicle-orientation-representations-for","title":"SoK: Vehicle Orientation Representations for Deep Rotation Estimation","date":"2021-12-08","arxiv_id":"2112.04421","repositories_listed":1,"syntology":null},{"url":"/paper/gatector-a-unified-framework-for-gaze-object","slug":"gatector-a-unified-framework-for-gaze-object","title":"GaTector: A Unified Framework for Gaze Object Prediction","date":"2021-12-07","arxiv_id":"2112.03549","repositories_listed":1,"syntology":null},{"url":"/paper/uniter-based-situated-coreference-resolution","slug":"uniter-based-situated-coreference-resolution","title":"UNITER-Based Situated Coreference Resolution with Rich Multimodal Input","date":"2021-12-07","arxiv_id":"2112.03521","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-transfer-attacks-for-object","slug":"context-aware-transfer-attacks-for-object","title":"Context-Aware Transfer Attacks for Object Detection","date":"2021-12-06","arxiv_id":"2112.03223","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-aware-transfer-attacks-for-object#ran","syntology_url":"https://syntology.ai/paper/2112.03223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.03223"}},"official":{"repos":["CSIPlab/context-aware-attacks"],"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/pttr-relational-3d-point-cloud-object","slug":"pttr-relational-3d-point-cloud-object","title":"PTTR: Relational 3D Point Cloud Object Tracking with Transformer","date":"2021-12-06","arxiv_id":"2112.02857","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-propagation-correction-modulation","slug":"reliable-propagation-correction-modulation","title":"Reliable Propagation-Correction Modulation for Video Object Segmentation","date":"2021-12-06","arxiv_id":"2112.02853","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reliable-propagation-correction-modulation#ran","syntology_url":"https://syntology.ai/paper/2112.02853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02853"}},"official":{"repos":["jerryx1110/rpcmvos"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sgm3d-stereo-guided-monocular-3d-object","slug":"sgm3d-stereo-guided-monocular-3d-object","title":"SGM3D: Stereo Guided Monocular 3D Object Detection","date":"2021-12-03","arxiv_id":"2112.01914","repositories_listed":1,"syntology":null},{"url":"/paper/the-box-size-confidence-bias-harms-your","slug":"the-box-size-confidence-bias-harms-your","title":"The Box Size Confidence Bias Harms Your Object Detector","date":"2021-12-03","arxiv_id":"2112.01901","repositories_listed":1,"syntology":null},{"url":"/paper/n-imagenet-towards-robust-fine-grained-object-1","slug":"n-imagenet-towards-robust-fine-grained-object-1","title":"N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras","date":"2021-12-02","arxiv_id":"2112.01041","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-monocular-depth-prediction-with","slug":"object-aware-monocular-depth-prediction-with","title":"Object-aware Monocular Depth Prediction with Instance Convolutions","date":"2021-12-02","arxiv_id":"2112.01521","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-unsupervised-image-captioning","slug":"object-centric-unsupervised-image-captioning","title":"Object-Centric Unsupervised Image Captioning","date":"2021-12-02","arxiv_id":"2112.00969","repositories_listed":1,"syntology":null},{"url":"/paper/partimagenet-a-large-high-quality-dataset-of","slug":"partimagenet-a-large-high-quality-dataset-of","title":"PartImageNet: A Large, High-Quality Dataset of Parts","date":"2021-12-02","arxiv_id":"2112.00933","repositories_listed":1,"syntology":null},{"url":"/paper/tise-a-toolbox-for-text-to-image-synthesis","slug":"tise-a-toolbox-for-text-to-image-synthesis","title":"TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation","date":"2021-12-02","arxiv_id":"2112.01398","repositories_listed":1,"syntology":null},{"url":"/paper/a-quantitative-approach-towards-german","slug":"a-quantitative-approach-towards-german","title":"A Quantitative Approach towards German Experiencer-Object Verbs","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/confidence-propagation-cluster-unleash-full","slug":"confidence-propagation-cluster-unleash-full","title":"Confidence Propagation Cluster: Unleash Full Potential of Object Detectors","date":"2021-12-01","arxiv_id":"2112.00342","repositories_listed":1,"syntology":null},{"url":"/paper/d-grasp-physically-plausible-dynamic-grasp","slug":"d-grasp-physically-plausible-dynamic-grasp","title":"D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions","date":"2021-12-01","arxiv_id":"2112.03028","repositories_listed":1,"syntology":null},{"url":"/paper/hypergraph-propagation-and-community","slug":"hypergraph-propagation-and-community","title":"Hypergraph Propagation and Community Selection for Objects Retrieval","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-cropping-for-self-supervised-1","slug":"object-aware-cropping-for-self-supervised-1","title":"Object-Aware Cropping for Self-Supervised Learning","date":"2021-12-01","arxiv_id":"2112.00319","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-video-language-pre-training-for","slug":"object-aware-video-language-pre-training-for","title":"Object-aware Video-language Pre-training for Retrieval","date":"2021-12-01","arxiv_id":"2112.00656","repositories_listed":1,"syntology":null},{"url":"/paper/airobject-a-temporally-evolving-graph","slug":"airobject-a-temporally-evolving-graph","title":"AirObject: A Temporally Evolving Graph Embedding for Object Identification","date":"2021-11-30","arxiv_id":"2111.15150","repositories_listed":1,"syntology":null},{"url":"/paper/epose-let-s-make-efficientpose-more-generally","slug":"epose-let-s-make-efficientpose-more-generally","title":"ePose: Let's Make EfficientPose More Generally Applicable","date":"2021-11-30","arxiv_id":"2111.15114","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-object-level-pre-training-with","slug":"contrastive-object-level-pre-training-with","title":"Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning","date":"2021-11-26","arxiv_id":"2111.13651","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-efficient-object-detection","slug":"revisiting-efficient-object-detection","title":"MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection","date":"2021-11-26","arxiv_id":"2111.13336","repositories_listed":1,"syntology":null},{"url":"/paper/cdnet-is-all-you-need-cascade-dcn-based","slug":"cdnet-is-all-you-need-cascade-dcn-based","title":"CDNet is all you need: Cascade DCN based underwater object detection RCNN","date":"2021-11-25","arxiv_id":"2111.12982","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-and-tracking-small-and-dense-moving","slug":"detecting-and-tracking-small-and-dense-moving","title":"Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark","date":"2021-11-25","arxiv_id":"2111.12960","repositories_listed":1,"syntology":null}],"record_sha256":"e027d1f300224792c5440dcdc2e6d8048a0e0c106478aea4dd1db50899d97271","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}