{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-detection-1/papers/20","list_of":"/task/object-detection-1","task":"object-detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":20,"pages_in_order":106,"rows_per_page":100,"rows":[1901,2000],"of":10514,"counts":{"archive_papers_tagged":10514,"with_a_code_link":4285,"where_syntology_ran_a_sample":1027,"not_listed_spam_title":0,"listed":10514,"listed_where_code_ran":1027,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":898,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":898,"listed_every_run_a_failure_of_syntologys_instrument":129,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-detection-1","prev":"/task/object-detection-1/papers/19","next":"/task/object-detection-1/papers/21","papers":[{"url":"/paper/ov-vg-a-benchmark-for-open-vocabulary-visual","slug":"ov-vg-a-benchmark-for-open-vocabulary-visual","title":"OV-VG: A Benchmark for Open-Vocabulary Visual Grounding","date":"2023-10-22","arxiv_id":"2310.14374","repositories_listed":1,"syntology":null},{"url":"/paper/the-importance-of-anti-aliasing-in-tiny","slug":"the-importance-of-anti-aliasing-in-tiny","title":"The Importance of Anti-Aliasing in Tiny Object Detection","date":"2023-10-22","arxiv_id":"2310.14221","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-transformer-using-cross-channel","slug":"multimodal-transformer-using-cross-channel","title":"Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images","date":"2023-10-21","arxiv_id":"2310.13876","repositories_listed":1,"syntology":null},{"url":"/paper/earlybird-early-fusion-for-multi-view","slug":"earlybird-early-fusion-for-multi-view","title":"EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View","date":"2023-10-20","arxiv_id":"2310.13350","repositories_listed":1,"syntology":null},{"url":"/paper/scalablemap-scalable-map-learning-for-online","slug":"scalablemap-scalable-map-learning-for-online","title":"ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction","date":"2023-10-20","arxiv_id":"2310.13378","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scalablemap-scalable-map-learning-for-online#ran","syntology_url":"https://syntology.ai/paper/2310.13378","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13378"}},"official":{"repos":["jingy1yu/scalablemap"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/zone-evaluation-revealing-spatial-bias-in","slug":"zone-evaluation-revealing-spatial-bias-in","title":"Zone Evaluation: Revealing Spatial Bias in Object Detection","date":"2023-10-20","arxiv_id":"2310.13215","repositories_listed":1,"syntology":null},{"url":"/paper/multi-camera-trajectory-matching-based-on","slug":"multi-camera-trajectory-matching-based-on","title":"Multi‑camera trajectory matching based on hierarchical clustering and constraints","date":"2023-10-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-rich-semantics-and-coarse","slug":"learning-from-rich-semantics-and-coarse","title":"Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection","date":"2023-10-18","arxiv_id":"2310.12152","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-from-rich-semantics-and-coarse#ran","syntology_url":"https://syntology.ai/paper/2310.12152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12152"}},"official":{"repos":["MengLcool/RichSem"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/geneval-an-object-focused-framework-for","slug":"geneval-an-object-focused-framework-for","title":"GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment","date":"2023-10-17","arxiv_id":"2310.11513","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/geneval-an-object-focused-framework-for#ran","syntology_url":"https://syntology.ai/paper/2310.11513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11513"}},"official":{"repos":["djghosh13/geneval"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monoskd-general-distillation-framework-for","slug":"monoskd-general-distillation-framework-for","title":"MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient","date":"2023-10-17","arxiv_id":"2310.11316","repositories_listed":1,"syntology":null},{"url":"/paper/towards-generalizable-multi-camera-3d-object","slug":"towards-generalizable-multi-camera-3d-object","title":"Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing","date":"2023-10-17","arxiv_id":"2310.11346","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/towards-generalizable-multi-camera-3d-object#ran","syntology_url":"https://syntology.ai/paper/2310.11346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11346"}},"official":{"repos":["envision-research/generalizable-bev"],"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/refconv-re-parameterized-refocusing","slug":"refconv-re-parameterized-refocusing","title":"RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets","date":"2023-10-16","arxiv_id":"2310.10563","repositories_listed":1,"syntology":null},{"url":"/paper/robollm-robotic-vision-tasks-grounded-on","slug":"robollm-robotic-vision-tasks-grounded-on","title":"RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models","date":"2023-10-16","arxiv_id":"2310.10221","repositories_listed":1,"syntology":null},{"url":"/paper/towards-open-world-active-learning-for-3d","slug":"towards-open-world-active-learning-for-3d","title":"Open-CRB: Towards Open World Active Learning for 3D Object Detection","date":"2023-10-16","arxiv_id":"2310.10391","repositories_listed":1,"syntology":null},{"url":"/paper/towards-open-world-co-salient-object","slug":"towards-open-world-co-salient-object","title":"Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking","date":"2023-10-16","arxiv_id":"2310.10264","repositories_listed":1,"syntology":null},{"url":"/paper/rank-detr-for-high-quality-object-detection","slug":"rank-detr-for-high-quality-object-detection","title":"Rank-DETR for High Quality Object Detection","date":"2023-10-13","arxiv_id":"2310.08854","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":1,"n_no_contract":1,"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, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rank-detr-for-high-quality-object-detection#ran","syntology_url":"https://syntology.ai/paper/2310.08854","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.08854"}},"official":{"repos":["leaplabthu/rank-detr"],"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/unipad-a-universal-pre-training-paradigm-for","slug":"unipad-a-universal-pre-training-paradigm-for","title":"UniPAD: A Universal Pre-training Paradigm for Autonomous Driving","date":"2023-10-12","arxiv_id":"2310.08370","repositories_listed":1,"syntology":null},{"url":"/paper/dual-radar-a-multi-modal-dataset-with-dual-4d","slug":"dual-radar-a-multi-modal-dataset-with-dual-4d","title":"Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving","date":"2023-10-11","arxiv_id":"2310.07602","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dual-radar-a-multi-modal-dataset-with-dual-4d#ran","syntology_url":"https://syntology.ai/paper/2310.07602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07602"}},"official":{"repos":["adept-thu/dual-radar"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-prior-knowledge-graphs-for","slug":"relational-prior-knowledge-graphs-for","title":"Relational Prior Knowledge Graphs for Detection and Instance Segmentation","date":"2023-10-11","arxiv_id":"2310.07573","repositories_listed":1,"syntology":null},{"url":"/paper/evit-an-eagle-vision-transformer-with-bi","slug":"evit-an-eagle-vision-transformer-with-bi","title":"EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention","date":"2023-10-10","arxiv_id":"2310.06629","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-synthetic-data-for-medical-vision","slug":"utilizing-synthetic-data-for-medical-vision","title":"Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images","date":"2023-10-10","arxiv_id":"2310.07027","repositories_listed":1,"syntology":null},{"url":"/paper/v2x-ahd-vehicle-to-everything-cooperation-1","slug":"v2x-ahd-vehicle-to-everything-cooperation-1","title":"V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network","date":"2023-10-10","arxiv_id":"2310.06603","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-intermediate-detector-decoupling-and-1","slug":"anchor-intermediate-detector-decoupling-and-1","title":"Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection","date":"2023-10-09","arxiv_id":"2310.05666","repositories_listed":1,"syntology":null},{"url":"/paper/care3d-an-active-3d-object-detection-dataset","slug":"care3d-an-active-3d-object-detection-dataset","title":"Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments","date":"2023-10-09","arxiv_id":"2310.05600","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-object-detection-with-2","slug":"semi-supervised-object-detection-with-2","title":"Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images","date":"2023-10-09","arxiv_id":"2310.05498","repositories_listed":1,"syntology":null},{"url":"/paper/hod-a-benchmark-dataset-for-harmful-object","slug":"hod-a-benchmark-dataset-for-harmful-object","title":"HOD: A Benchmark Dataset for Harmful Object Detection","date":"2023-10-08","arxiv_id":"2310.05192","repositories_listed":1,"syntology":null},{"url":"/paper/instructdet-diversifying-referring-object","slug":"instructdet-diversifying-referring-object","title":"InstructDET: Diversifying Referring Object Detection with Generalized Instructions","date":"2023-10-08","arxiv_id":"2310.05136","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/instructdet-diversifying-referring-object#ran","syntology_url":"https://syntology.ai/paper/2310.05136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05136"}},"official":{"repos":["jyfenggogo/instructdet"],"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/hallucidet-hallucinating-rgb-modality-for","slug":"hallucidet-hallucinating-rgb-modality-for","title":"HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information","date":"2023-10-07","arxiv_id":"2310.04662","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-effectively-train-an-ensemble-of","slug":"how-to-effectively-train-an-ensemble-of","title":"How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty","date":"2023-10-07","arxiv_id":"2310.04829","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-camouflaged-object-detection-a","slug":"collaborative-camouflaged-object-detection-a","title":"Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark","date":"2023-10-06","arxiv_id":"2310.04253","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-grasp-from-somewhere-to-anywhere","slug":"learning-to-grasp-from-somewhere-to-anywhere","title":"Toward a Plug-and-Play Vision-Based Grasping Module for Robotics","date":"2023-10-06","arxiv_id":"2310.04349","repositories_listed":1,"syntology":null},{"url":"/paper/wlst-weak-labels-guided-self-training-for","slug":"wlst-weak-labels-guided-self-training-for","title":"WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection","date":"2023-10-05","arxiv_id":"2310.03821","repositories_listed":1,"syntology":null},{"url":"/paper/cobev-elevating-roadside-3d-object-detection","slug":"cobev-elevating-roadside-3d-object-detection","title":"CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity","date":"2023-10-04","arxiv_id":"2310.02815","repositories_listed":1,"syntology":null},{"url":"/paper/coda-collaborative-novel-box-discovery-and-1","slug":"coda-collaborative-novel-box-discovery-and-1","title":"CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection","date":"2023-10-04","arxiv_id":"2310.02960","repositories_listed":1,"syntology":null},{"url":"/paper/magicdrive-street-view-generation-with","slug":"magicdrive-street-view-generation-with","title":"MagicDrive: Street View Generation with Diverse 3D Geometry Control","date":"2023-10-04","arxiv_id":"2310.02601","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/magicdrive-street-view-generation-with#ran","syntology_url":"https://syntology.ai/paper/2310.02601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02601"}},"official":null}},{"url":"/paper/vit-reciprocam-gradient-and-attention-free","slug":"vit-reciprocam-gradient-and-attention-free","title":"ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformer","date":"2023-10-04","arxiv_id":"2310.02588","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-the-benchmark-detecting-diverse","slug":"beyond-the-benchmark-detecting-diverse","title":"Beyond the Benchmark: Detecting Diverse Anomalies in Videos","date":"2023-10-03","arxiv_id":"2310.01904","repositories_listed":1,"syntology":null},{"url":"/paper/darth-holistic-test-time-adaptation-for-1","slug":"darth-holistic-test-time-adaptation-for-1","title":"DARTH: Holistic Test-time Adaptation for Multiple Object Tracking","date":"2023-10-03","arxiv_id":"2310.01926","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/darth-holistic-test-time-adaptation-for-1#ran","syntology_url":"https://syntology.ai/paper/2310.01926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01926"}},"official":{"repos":["mattiasegu/darth"],"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/exploring-model-learning-heterogeneity-for","slug":"exploring-model-learning-heterogeneity-for","title":"Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness","date":"2023-10-03","arxiv_id":"2310.02237","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-visual-scene-understanding","slug":"adaptive-visual-scene-understanding","title":"Adaptive Visual Scene Understanding: Incremental Scene Graph Generation","date":"2023-10-02","arxiv_id":"2310.01636","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adaptive-visual-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/2310.01636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01636"}},"official":{"repos":["zhanglab-deepneurocoglab/csegg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/clipself-vision-transformer-distills-itself","slug":"clipself-vision-transformer-distills-itself","title":"CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction","date":"2023-10-02","arxiv_id":"2310.01403","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/clipself-vision-transformer-distills-itself#ran","syntology_url":"https://syntology.ai/paper/2310.01403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01403"}},"official":{"repos":["wusize/clipself"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dst-det-simple-dynamic-self-training-for-open","slug":"dst-det-simple-dynamic-self-training-for-open","title":"DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection","date":"2023-10-02","arxiv_id":"2310.01393","repositories_listed":1,"syntology":null},{"url":"/paper/you-only-look-at-once-for-real-time-and","slug":"you-only-look-at-once-for-real-time-and","title":"You Only Look at Once for Real-time and Generic Multi-Task","date":"2023-10-02","arxiv_id":"2310.01641","repositories_listed":1,"syntology":null},{"url":"/paper/horizontal-class-backdoor-to-deep-learning","slug":"horizontal-class-backdoor-to-deep-learning","title":"Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense","date":"2023-10-01","arxiv_id":"2310.00542","repositories_listed":1,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":10,"n_instrument":6,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":7,"phrase":"16 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; 6 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/horizontal-class-backdoor-to-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2310.00542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00542"}},"official":{"repos":["shihe98/hcb"],"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":["found_in_text","official"]}}},{"url":"/paper/self-supervised-learning-of-contextualized","slug":"self-supervised-learning-of-contextualized","title":"Self-supervised Learning of Contextualized Local Visual Embeddings","date":"2023-10-01","arxiv_id":"2310.00527","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-of-contextualized#ran","syntology_url":"https://syntology.ai/paper/2310.00527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00527"}},"official":{"repos":["sthalles/clove"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/instructcv-instruction-tuned-text-to-image","slug":"instructcv-instruction-tuned-text-to-image","title":"InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists","date":"2023-09-30","arxiv_id":"2310.00390","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/instructcv-instruction-tuned-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2310.00390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00390"}},"official":{"repos":["AlaaLab/InstructCV"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/see-beyond-seeing-robust-3d-object-detection","slug":"see-beyond-seeing-robust-3d-object-detection","title":"Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation","date":"2023-09-29","arxiv_id":"2309.17336","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/see-beyond-seeing-robust-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2309.17336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.17336"}},"official":{"repos":["djning/see_beyond_seeing"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hic-yolov5-improved-yolov5-for-small-object","slug":"hic-yolov5-improved-yolov5-for-small-object","title":"HIC-YOLOv5: Improved YOLOv5 For Small Object Detection","date":"2023-09-28","arxiv_id":"2309.16393","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-dataset-for-localization-mapping","slug":"multimodal-dataset-for-localization-mapping","title":"Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms","date":"2023-09-27","arxiv_id":"2309.15332","repositories_listed":1,"syntology":null},{"url":"/paper/distillbev-boosting-multi-camera-3d-object","slug":"distillbev-boosting-multi-camera-3d-object","title":"DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation","date":"2023-09-26","arxiv_id":"2309.15109","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/distillbev-boosting-multi-camera-3d-object#ran","syntology_url":"https://syntology.ai/paper/2309.15109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15109"}},"official":{"repos":["qcraftai/distill-bev"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mocae-mixture-of-calibrated-experts","slug":"mocae-mixture-of-calibrated-experts","title":"MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection","date":"2023-09-26","arxiv_id":"2309.14976","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/mocae-mixture-of-calibrated-experts#ran","syntology_url":"https://syntology.ai/paper/2309.14976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14976"}},"official":{"repos":["fiveai/MoCaE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-source-domain-adaptation-for-object-1","slug":"multi-source-domain-adaptation-for-object-1","title":"Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher","date":"2023-09-26","arxiv_id":"2309.14950","repositories_listed":1,"syntology":null},{"url":"/paper/masked-image-residual-learning-for-scaling-1","slug":"masked-image-residual-learning-for-scaling-1","title":"Masked Image Residual Learning for Scaling Deeper Vision Transformers","date":"2023-09-25","arxiv_id":"2309.14136","repositories_listed":1,"syntology":null},{"url":"/paper/unibev-multi-modal-3d-object-detection-with","slug":"unibev-multi-modal-3d-object-detection-with","title":"UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities","date":"2023-09-25","arxiv_id":"2309.14516","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":3,"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/unibev-multi-modal-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2309.14516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14516"}},"official":{"repos":["tudelft-iv/unibev"],"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/semi-supervised-domain-generalization-for-1","slug":"semi-supervised-domain-generalization-for-1","title":"Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment","date":"2023-09-24","arxiv_id":"2309.13525","repositories_listed":1,"syntology":null},{"url":"/paper/bgf-yolo-enhanced-yolov8-with-multiscale","slug":"bgf-yolo-enhanced-yolov8-with-multiscale","title":"BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection","date":"2023-09-22","arxiv_id":"2309.12585","repositories_listed":1,"syntology":null},{"url":"/paper/clusterformer-clustering-as-a-universal","slug":"clusterformer-clustering-as-a-universal","title":"ClusterFormer: Clustering As A Universal Visual Learner","date":"2023-09-22","arxiv_id":"2309.13196","repositories_listed":1,"syntology":null},{"url":"/paper/detect-every-thing-with-few-examples","slug":"detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","arxiv_id":"2309.12969","repositories_listed":1,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":1,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/detect-every-thing-with-few-examples#ran","syntology_url":"https://syntology.ai/paper/2309.12969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12969"}},"official":{"repos":["mlzxy/devit"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/misfit-v-misaligned-image-synthesis-and","slug":"misfit-v-misaligned-image-synthesis-and","title":"MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and Visual","date":"2023-09-22","arxiv_id":"2309.13216","repositories_listed":1,"syntology":null},{"url":"/paper/fgfusion-fine-grained-lidar-camera-fusion-for","slug":"fgfusion-fine-grained-lidar-camera-fusion-for","title":"FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection","date":"2023-09-21","arxiv_id":"2309.11804","repositories_listed":1,"syntology":null},{"url":"/paper/monouni-a-unified-vehicle-and-infrastructure","slug":"monouni-a-unified-vehicle-and-infrastructure","title":"MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues","date":"2023-09-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-domain-adaptation-for-self","slug":"unsupervised-domain-adaptation-for-self","title":"Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features","date":"2023-09-21","arxiv_id":"2309.12140","repositories_listed":1,"syntology":null},{"url":"/paper/eptq-enhanced-post-training-quantization-via","slug":"eptq-enhanced-post-training-quantization-via","title":"EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization","date":"2023-09-20","arxiv_id":"2309.11531","repositories_listed":1,"syntology":null},{"url":"/paper/h2o-heatmap-by-hierarchical-occlusion","slug":"h2o-heatmap-by-hierarchical-occlusion","title":"H²O: Heatmap by Hierarchical Occlusion","date":"2023-09-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rmt-retentive-networks-meet-vision","slug":"rmt-retentive-networks-meet-vision","title":"RMT: Retentive Networks Meet Vision Transformers","date":"2023-09-20","arxiv_id":"2309.11523","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":3,"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/rmt-retentive-networks-meet-vision#ran","syntology_url":"https://syntology.ai/paper/2309.11523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11523"}},"official":{"repos":["qhfan/RMT"],"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/shape-anchor-guided-holistic-indoor-scene","slug":"shape-anchor-guided-holistic-indoor-scene","title":"Shape Anchor Guided Holistic Indoor Scene Understanding","date":"2023-09-20","arxiv_id":"2309.11133","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":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) · 1 unverified","sample_list":"/paper/shape-anchor-guided-holistic-indoor-scene#ran","syntology_url":"https://syntology.ai/paper/2309.11133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11133"}},"official":{"repos":["Geo-Tell/AncRec"],"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/few-shot-object-detection-in-remote-sensing","slug":"few-shot-object-detection-in-remote-sensing","title":"Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects","date":"2023-09-19","arxiv_id":"2309.10588","repositories_listed":1,"syntology":null},{"url":"/paper/spot-scalable-3d-pre-training-via-occupancy","slug":"spot-scalable-3d-pre-training-via-occupancy","title":"SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations","date":"2023-09-19","arxiv_id":"2309.10527","repositories_listed":1,"syntology":null},{"url":"/paper/dformer-rethinking-rgbd-representation","slug":"dformer-rethinking-rgbd-representation","title":"DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation","date":"2023-09-18","arxiv_id":"2309.09668","repositories_listed":1,"syntology":null},{"url":"/paper/drawing-the-same-bounding-box-twice-coping","slug":"drawing-the-same-bounding-box-twice-coping","title":"Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels","date":"2023-09-18","arxiv_id":"2309.09742","repositories_listed":1,"syntology":null},{"url":"/paper/medl-u-uncertainty-aware-3d-automatic","slug":"medl-u-uncertainty-aware-3d-automatic","title":"MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning","date":"2023-09-18","arxiv_id":"2309.09599","repositories_listed":1,"syntology":null},{"url":"/paper/moving-object-detection-and-tracking-with-4d","slug":"moving-object-detection-and-tracking-with-4d","title":"RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud","date":"2023-09-18","arxiv_id":"2309.09737","repositories_listed":1,"syntology":null},{"url":"/paper/chasing-day-and-night-towards-robust-and","slug":"chasing-day-and-night-towards-robust-and","title":"Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera","date":"2023-09-17","arxiv_id":"2309.09297","repositories_listed":1,"syntology":null},{"url":"/paper/egoobjects-a-large-scale-egocentric-dataset","slug":"egoobjects-a-large-scale-egocentric-dataset","title":"EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding","date":"2023-09-15","arxiv_id":"2309.08816","repositories_listed":1,"syntology":null},{"url":"/paper/m-3-net-multilevel-mixed-and-multistage","slug":"m-3-net-multilevel-mixed-and-multistage","title":"M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection","date":"2023-09-15","arxiv_id":"2309.08365","repositories_listed":1,"syntology":null},{"url":"/paper/salient-object-detection-in-optical-remote","slug":"salient-object-detection-in-optical-remote","title":"Salient Object Detection in Optical Remote Sensing Images Driven by Transformer","date":"2023-09-15","arxiv_id":"2309.08206","repositories_listed":1,"syntology":null},{"url":"/paper/alwod-active-learning-for-weakly-supervised","slug":"alwod-active-learning-for-weakly-supervised","title":"ALWOD: Active Learning for Weakly-Supervised Object Detection","date":"2023-09-14","arxiv_id":"2309.07914","repositories_listed":1,"syntology":null},{"url":"/paper/ccspnet-joint-efficient-joint-training-method","slug":"ccspnet-joint-efficient-joint-training-method","title":"CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions","date":"2023-09-13","arxiv_id":"2309.06902","repositories_listed":1,"syntology":null},{"url":"/paper/supfusion-supervised-lidar-camera-fusion-for","slug":"supfusion-supervised-lidar-camera-fusion-for","title":"SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection","date":"2023-09-13","arxiv_id":"2309.07084","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-generation-harnessing-text-to-image","slug":"beyond-generation-harnessing-text-to-image","title":"Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation","date":"2023-09-12","arxiv_id":"2309.05956","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/beyond-generation-harnessing-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2309.05956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05956"}},"official":{"repos":["gyhandy/text2image-for-detection"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/gall-bladder-cancer-detection-from-us-images","slug":"gall-bladder-cancer-detection-from-us-images","title":"Gall Bladder Cancer Detection from US Images with Only Image Level Labels","date":"2023-09-11","arxiv_id":"2309.05261","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-co-salient-object-detection","slug":"zero-shot-co-salient-object-detection","title":"Zero-Shot Co-salient Object Detection Framework","date":"2023-09-11","arxiv_id":"2309.05499","repositories_listed":1,"syntology":null},{"url":"/paper/transformers-in-small-object-detection-a","slug":"transformers-in-small-object-detection-a","title":"Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art","date":"2023-09-10","arxiv_id":"2309.04902","repositories_listed":1,"syntology":null},{"url":"/paper/sortedap-rethinking-evaluation-metrics-for","slug":"sortedap-rethinking-evaluation-metrics-for","title":"SortedAP: Rethinking evaluation metrics for instance segmentation","date":"2023-09-09","arxiv_id":"2309.04887","repositories_listed":1,"syntology":null},{"url":"/paper/fishmot-a-simple-and-effective-method-for","slug":"fishmot-a-simple-and-effective-method-for","title":"FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching","date":"2023-09-06","arxiv_id":"2309.02975","repositories_listed":1,"syntology":null},{"url":"/paper/anatomy-driven-pathology-detection-on-chest-x","slug":"anatomy-driven-pathology-detection-on-chest-x","title":"Anatomy-Driven Pathology Detection on Chest X-rays","date":"2023-09-05","arxiv_id":"2309.02578","repositories_listed":1,"syntology":null},{"url":"/paper/compressing-vision-transformers-for-low","slug":"compressing-vision-transformers-for-low","title":"Compressing Vision Transformers for Low-Resource Visual Learning","date":"2023-09-05","arxiv_id":"2309.02617","repositories_listed":1,"syntology":null},{"url":"/paper/the-adversarial-implications-of-variable-time","slug":"the-adversarial-implications-of-variable-time","title":"The Adversarial Implications of Variable-Time Inference","date":"2023-09-05","arxiv_id":"2309.02159","repositories_listed":1,"syntology":null},{"url":"/paper/large-separable-kernel-attention-rethinking","slug":"large-separable-kernel-attention-rethinking","title":"Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN","date":"2023-09-04","arxiv_id":"2309.01439","repositories_listed":1,"syntology":null},{"url":"/paper/snow-removal-for-lidar-point-clouds-with","slug":"snow-removal-for-lidar-point-clouds-with","title":"Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random Fields","date":"2023-09-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mila-memory-based-instance-level-adaptation-1","slug":"mila-memory-based-instance-level-adaptation-1","title":"MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection","date":"2023-09-03","arxiv_id":"2309.01086","repositories_listed":1,"syntology":null},{"url":"/paper/ntu4dradlm-4d-radar-centric-multi-modal-1","slug":"ntu4dradlm-4d-radar-centric-multi-modal-1","title":"NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping","date":"2023-09-02","arxiv_id":"2309.00962","repositories_listed":1,"syntology":null},{"url":"/paper/objectlab-automated-diagnosis-of-mislabeled","slug":"objectlab-automated-diagnosis-of-mislabeled","title":"ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data","date":"2023-09-02","arxiv_id":"2309.00832","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/objectlab-automated-diagnosis-of-mislabeled#ran","syntology_url":"https://syntology.ai/paper/2309.00832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00832"}},"official":{"repos":["cleanlab/cleanlab"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/revcolv2-exploring-disentangled-1","slug":"revcolv2-exploring-disentangled-1","title":"RevColV2: Exploring Disentangled Representations in Masked Image Modeling","date":"2023-09-02","arxiv_id":"2309.01005","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-multiple-object-tracking","slug":"object-centric-multiple-object-tracking","title":"Object-Centric Multiple Object Tracking","date":"2023-09-01","arxiv_id":"2309.00233","repositories_listed":1,"syntology":null},{"url":"/paper/openins3d-snap-and-lookup-for-3d-open","slug":"openins3d-snap-and-lookup-for-3d-open","title":"OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation","date":"2023-09-01","arxiv_id":"2309.00616","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/openins3d-snap-and-lookup-for-3d-open#ran","syntology_url":"https://syntology.ai/paper/2309.00616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00616"}},"official":{"repos":["Pointcept/OpenIns3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/an-energy-aware-approach-to-design-self","slug":"an-energy-aware-approach-to-design-self","title":"An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge","date":"2023-08-31","arxiv_id":"2309.00022","repositories_listed":1,"syntology":null},{"url":"/paper/njobvu-ai-an-open-source-tool-for","slug":"njobvu-ai-an-open-source-tool-for","title":"Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision models","date":"2023-08-31","arxiv_id":"2308.16435","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-recognition-of-unknown-objects","slug":"unsupervised-recognition-of-unknown-objects","title":"Unsupervised Recognition of Unknown Objects for Open-World Object Detection","date":"2023-08-31","arxiv_id":"2308.16527","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":3,"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/unsupervised-recognition-of-unknown-objects#ran","syntology_url":"https://syntology.ai/paper/2308.16527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.16527"}},"official":{"repos":["frh23333/mepu-owod"],"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/circleformer-circular-nuclei-detection-in","slug":"circleformer-circular-nuclei-detection-in","title":"CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention","date":"2023-08-30","arxiv_id":"2308.16145","repositories_listed":1,"syntology":null}],"record_sha256":"52fa9b144e794b9b88c01916d140437bfea1c29aa833106044ccc4f8d336975b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}