{"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/17","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":17,"pages_in_order":107,"rows_per_page":100,"rows":[1601,1700],"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/16","next":"/task/object/papers/18","papers":[{"url":"/paper/pre-training-lidar-based-3d-object-detectors","slug":"pre-training-lidar-based-3d-object-detectors","title":"Pre-Training LiDAR-Based 3D Object Detectors Through Colorization","date":"2023-10-23","arxiv_id":"2310.14592","repositories_listed":1,"syntology":null},{"url":"/paper/deep-mdp-a-modular-framework-for-multi-object","slug":"deep-mdp-a-modular-framework-for-multi-object","title":"Deep MDP: A Modular Framework for Multi-Object Tracking","date":"2023-10-22","arxiv_id":"2310.14294","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/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/lidar-panoptic-segmentation-and-tracking","slug":"lidar-panoptic-segmentation-and-tracking","title":"Lidar Panoptic Segmentation and Tracking without Bells and Whistles","date":"2023-10-19","arxiv_id":"2310.12464","repositories_listed":1,"syntology":null},{"url":"/paper/pga-personalizing-grasping-agents-with-single","slug":"pga-personalizing-grasping-agents-with-single","title":"PGA: Personalizing Grasping Agents with Single Human-Robot Interaction","date":"2023-10-19","arxiv_id":"2310.12547","repositories_listed":1,"syntology":null},{"url":"/paper/putting-the-object-back-into-video-object","slug":"putting-the-object-back-into-video-object","title":"Putting the Object Back into Video Object Segmentation","date":"2023-10-19","arxiv_id":"2310.12982","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/putting-the-object-back-into-video-object#ran","syntology_url":"https://syntology.ai/paper/2310.12982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12982"}},"official":{"repos":["hkchengrex/Cutie"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-object-localization-in-the-era","slug":"unsupervised-object-localization-in-the-era","title":"Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey","date":"2023-10-19","arxiv_id":"2310.12904","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-inversion-and-reassembly-for","slug":"object-aware-inversion-and-reassembly-for","title":"Object-aware Inversion and Reassembly for Image Editing","date":"2023-10-18","arxiv_id":"2310.12149","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/object-aware-inversion-and-reassembly-for#ran","syntology_url":"https://syntology.ai/paper/2310.12149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12149"}},"official":{"repos":["aim-uofa/OIR"],"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/revamp-automated-simulations-of-adversarial","slug":"revamp-automated-simulations-of-adversarial","title":"REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes","date":"2023-10-18","arxiv_id":"2310.12243","repositories_listed":1,"syntology":null},{"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/consistnet-enforcing-3d-consistency-for-multi","slug":"consistnet-enforcing-3d-consistency-for-multi","title":"ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion","date":"2023-10-16","arxiv_id":"2310.10343","repositories_listed":1,"syntology":null},{"url":"/paper/llm-blueprint-enabling-text-to-image","slug":"llm-blueprint-enabling-text-to-image","title":"LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts","date":"2023-10-16","arxiv_id":"2310.10640","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/llm-blueprint-enabling-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2310.10640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10640"}},"official":{"repos":["hananshafi/llmblueprint"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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/zero-shot-object-goal-visual-navigation-with","slug":"zero-shot-object-goal-visual-navigation-with","title":"Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network","date":"2023-10-15","arxiv_id":"2310.09883","repositories_listed":1,"syntology":null},{"url":"/paper/obsum-an-object-based-spatial-unmixing-model","slug":"obsum-an-object-based-spatial-unmixing-model","title":"OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images","date":"2023-10-14","arxiv_id":"2310.09517","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/think-act-and-ask-open-world-interactive","slug":"think-act-and-ask-open-world-interactive","title":"Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation","date":"2023-10-12","arxiv_id":"2310.07968","repositories_listed":1,"syntology":null},{"url":"/paper/cribo-self-supervised-learning-via-cross","slug":"cribo-self-supervised-learning-via-cross","title":"CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping","date":"2023-10-11","arxiv_id":"2310.07855","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cribo-self-supervised-learning-via-cross#ran","syntology_url":"https://syntology.ai/paper/2310.07855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07855"}},"official":{"repos":["tileb1/cribo"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deepsimho-stable-pose-estimation-for-hand","slug":"deepsimho-stable-pose-estimation-for-hand","title":"DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation","date":"2023-10-11","arxiv_id":"2310.07206","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/deepsimho-stable-pose-estimation-for-hand#ran","syntology_url":"https://syntology.ai/paper/2310.07206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07206"}},"official":{"repos":["rongakowang/deepsimho"],"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/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/cross-modal-cognitive-consensus-guided-audio","slug":"cross-modal-cognitive-consensus-guided-audio","title":"Cross-modal Cognitive Consensus guided Audio-Visual Segmentation","date":"2023-10-10","arxiv_id":"2310.06259","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cross-modal-cognitive-consensus-guided-audio#ran","syntology_url":"https://syntology.ai/paper/2310.06259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06259"}},"official":{"repos":["zhaofengshi/avs-c3n"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dips-discriminative-pseudo-label-sampling","slug":"dips-discriminative-pseudo-label-sampling","title":"DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization","date":"2023-10-09","arxiv_id":"2310.06196","repositories_listed":1,"syntology":null},{"url":"/paper/ipdreamer-appearance-controllable-3d-object","slug":"ipdreamer-appearance-controllable-3d-object","title":"IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts","date":"2023-10-09","arxiv_id":"2310.05375","repositories_listed":1,"syntology":null},{"url":"/paper/provable-compositional-generalization-for","slug":"provable-compositional-generalization-for","title":"Provable Compositional Generalization for Object-Centric Learning","date":"2023-10-09","arxiv_id":"2310.05327","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/provable-compositional-generalization-for#ran","syntology_url":"https://syntology.ai/paper/2310.05327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05327"}},"official":{"repos":["brendel-group/objects-compositional-generalization"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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/cad-models-to-real-world-images-a-practical","slug":"cad-models-to-real-world-images-a-practical","title":"CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification","date":"2023-10-07","arxiv_id":"2310.04757","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/contactgen-generative-contact-modeling-for-1","slug":"contactgen-generative-contact-modeling-for-1","title":"ContactGen: Generative Contact Modeling for Grasp Generation","date":"2023-10-05","arxiv_id":"2310.03740","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"6 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/contactgen-generative-contact-modeling-for-1#ran","syntology_url":"https://syntology.ai/paper/2310.03740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.03740"}},"official":{"repos":["stevenlsw/contactgen"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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/land-cover-change-detection-using-paired","slug":"land-cover-change-detection-using-paired","title":"ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer","date":"2023-10-04","arxiv_id":"2310.02674","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/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/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/offline-tracking-with-object-permanence","slug":"offline-tracking-with-object-permanence","title":"Offline Tracking with Object Permanence","date":"2023-10-02","arxiv_id":"2310.01288","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-and-mitigating-object-hallucination","slug":"analyzing-and-mitigating-object-hallucination","title":"Analyzing and Mitigating Object Hallucination in Large Vision-Language Models","date":"2023-10-01","arxiv_id":"2310.00754","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":8,"phrase":"7 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/analyzing-and-mitigating-object-hallucination#ran","syntology_url":"https://syntology.ai/paper/2310.00754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00754"}},"official":{"repos":["yiyangzhou/lure"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"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/context-i2w-mapping-images-to-context","slug":"context-i2w-mapping-images-to-context","title":"Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval","date":"2023-09-28","arxiv_id":"2309.16137","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/context-i2w-mapping-images-to-context#ran","syntology_url":"https://syntology.ai/paper/2309.16137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16137"}},"official":{"repos":["pter61/context-i2w"],"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/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/learning-to-terminate-in-object-navigation","slug":"learning-to-terminate-in-object-navigation","title":"Learning to Terminate in Object Navigation","date":"2023-09-28","arxiv_id":"2309.16164","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-entity-grounding-with-open","slug":"context-aware-entity-grounding-with-open","title":"Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs","date":"2023-09-27","arxiv_id":"2309.15940","repositories_listed":1,"syntology":null},{"url":"/paper/enigma-51-towards-a-fine-grained","slug":"enigma-51-towards-a-fine-grained","title":"ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios","date":"2023-09-26","arxiv_id":"2309.14809","repositories_listed":1,"syntology":null},{"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/treating-motion-as-option-with-output","slug":"treating-motion-as-option-with-output","title":"Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation","date":"2023-09-26","arxiv_id":"2309.14786","repositories_listed":1,"syntology":null},{"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/rethinking-amodal-video-segmentation-from","slug":"rethinking-amodal-video-segmentation-from","title":"Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation","date":"2023-09-23","arxiv_id":"2309.13248","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-knowledge-embedded","slug":"spatial-temporal-knowledge-embedded","title":"Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation","date":"2023-09-23","arxiv_id":"2309.13237","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/lmc-large-model-collaboration-with-cross-1","slug":"lmc-large-model-collaboration-with-cross-1","title":"LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition","date":"2023-09-22","arxiv_id":"2309.12780","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lmc-large-model-collaboration-with-cross-1#ran","syntology_url":"https://syntology.ai/paper/2309.12780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12780"}},"official":{"repos":["harryqu123/lmc"],"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/nerrf-3d-reconstruction-and-view-synthesis","slug":"nerrf-3d-reconstruction-and-view-synthesis","title":"NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields","date":"2023-09-22","arxiv_id":"2309.13039","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"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) · 2 unverified","sample_list":"/paper/nerrf-3d-reconstruction-and-view-synthesis#ran","syntology_url":"https://syntology.ai/paper/2309.13039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13039"}},"official":{"repos":["dawning77/nerrf"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/noc-high-quality-neural-object-cloning-with","slug":"noc-high-quality-neural-object-cloning-with","title":"NTO3D: Neural Target Object 3D Reconstruction with Segment Anything","date":"2023-09-22","arxiv_id":"2309.12790","repositories_listed":1,"syntology":null},{"url":"/paper/moda-leveraging-motion-priors-from-videos-for","slug":"moda-leveraging-motion-priors-from-videos-for","title":"MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation","date":"2023-09-21","arxiv_id":"2309.11711","repositories_listed":1,"syntology":null},{"url":"/paper/neurallabeling-a-versatile-toolset-for","slug":"neurallabeling-a-versatile-toolset-for","title":"NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields","date":"2023-09-21","arxiv_id":"2309.11966","repositories_listed":1,"syntology":null},{"url":"/paper/language-driven-object-fusion-into-neural","slug":"language-driven-object-fusion-into-neural","title":"Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates","date":"2023-09-20","arxiv_id":"2309.11281","repositories_listed":1,"syntology":null},{"url":"/paper/rgb-based-category-level-object-pose","slug":"rgb-based-category-level-object-pose","title":"RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery","date":"2023-09-19","arxiv_id":"2309.10255","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rgb-based-category-level-object-pose#ran","syntology_url":"https://syntology.ai/paper/2309.10255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10255"}},"official":{"repos":["goldoak/DMSR"],"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/reasoning-about-the-unseen-for-efficient","slug":"reasoning-about-the-unseen-for-efficient","title":"Reasoning about the Unseen for Efficient Outdoor Object Navigation","date":"2023-09-18","arxiv_id":"2309.10103","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/reasoning-about-the-unseen-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2309.10103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10103"}},"official":{"repos":["quantingxie/reasonedexplorer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-open-vocabulary-object","slug":"unsupervised-open-vocabulary-object","title":"Unsupervised Open-Vocabulary Object Localization in Videos","date":"2023-09-18","arxiv_id":"2309.09858","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-open-vocabulary-object#ran","syntology_url":"https://syntology.ai/paper/2309.09858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.09858"}},"official":{"repos":["amazon-science/object-centric-vol"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fdcnet-feature-drift-compensation-network-for","slug":"fdcnet-feature-drift-compensation-network-for","title":"FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization","date":"2023-09-17","arxiv_id":"2309.09122","repositories_listed":1,"syntology":null},{"url":"/paper/affordpose-a-large-scale-dataset-of-hand","slug":"affordpose-a-large-scale-dataset-of-hand","title":"AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose","date":"2023-09-16","arxiv_id":"2309.08942","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/affordpose-a-large-scale-dataset-of-hand#ran","syntology_url":"https://syntology.ai/paper/2309.08942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.08942"}},"official":{"repos":["gentlesjan/affordpose"],"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/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/find-what-you-want-learning-demand-1","slug":"find-what-you-want-learning-demand-1","title":"Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation","date":"2023-09-15","arxiv_id":"2309.08138","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":3,"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 3 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/find-what-you-want-learning-demand-1#ran","syntology_url":"https://syntology.ai/paper/2309.08138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.08138"}},"official":{"repos":["whcpumpkin/demand-driven-navigation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ycb-ev-event-vision-dataset-for-6dof-object","slug":"ycb-ev-event-vision-dataset-for-6dof-object","title":"YCB-Ev 1.1: Event-vision dataset for 6DoF object pose estimation","date":"2023-09-15","arxiv_id":"2309.08482","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/handnerf-learning-to-reconstruct-hand-object","slug":"handnerf-learning-to-reconstruct-hand-object","title":"HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image","date":"2023-09-14","arxiv_id":"2309.07891","repositories_listed":1,"syntology":null},{"url":"/paper/prograsp-pragmatic-human-robot-communication","slug":"prograsp-pragmatic-human-robot-communication","title":"PROGrasp: Pragmatic Human-Robot Communication for Object Grasping","date":"2023-09-14","arxiv_id":"2309.07759","repositories_listed":1,"syntology":null},{"url":"/paper/limited-angle-tomography-reconstruction-via","slug":"limited-angle-tomography-reconstruction-via","title":"Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data","date":"2023-09-13","arxiv_id":"2309.06948","repositories_listed":1,"syntology":null},{"url":"/paper/transparent-object-tracking-with-enhanced","slug":"transparent-object-tracking-with-enhanced","title":"Transparent Object Tracking with Enhanced Fusion Module","date":"2023-09-13","arxiv_id":"2309.06701","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/towards-high-quality-specular-highlight","slug":"towards-high-quality-specular-highlight","title":"Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data","date":"2023-09-12","arxiv_id":"2309.06302","repositories_listed":1,"syntology":null},{"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/learning-geometric-representations-of-objects","slug":"learning-geometric-representations-of-objects","title":"Learning Geometric Representations of Objects via Interaction","date":"2023-09-11","arxiv_id":"2309.05346","repositories_listed":1,"syntology":null},{"url":"/paper/mobile-vision-transformer-based-visual-object","slug":"mobile-vision-transformer-based-visual-object","title":"Mobile Vision Transformer-based Visual Object Tracking","date":"2023-09-11","arxiv_id":"2309.05829","repositories_listed":1,"syntology":null},{"url":"/paper/multi3drefer-grounding-text-description-to","slug":"multi3drefer-grounding-text-description-to","title":"Multi3DRefer: Grounding Text Description to Multiple 3D Objects","date":"2023-09-11","arxiv_id":"2309.05251","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/multi3drefer-grounding-text-description-to#ran","syntology_url":"https://syntology.ai/paper/2309.05251","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05251"}},"official":{"repos":["3dlg-hcvc/M3DRef-CLIP"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"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/a-skeleton-based-approach-for-rock-crack","slug":"a-skeleton-based-approach-for-rock-crack","title":"A Skeleton-based Approach For Rock Crack Detection Towards A Climbing Robot Application","date":"2023-09-10","arxiv_id":"2309.05139","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/language-prompt-for-autonomous-driving","slug":"language-prompt-for-autonomous-driving","title":"Language Prompt for Autonomous Driving","date":"2023-09-08","arxiv_id":"2309.04379","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":1,"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/language-prompt-for-autonomous-driving#ran","syntology_url":"https://syntology.ai/paper/2309.04379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04379"}},"official":{"repos":["wudongming97/prompt4driving"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/unsupervised-object-localization-with","slug":"unsupervised-object-localization-with","title":"Unsupervised Object Localization with Representer Point Selection","date":"2023-09-08","arxiv_id":"2309.04172","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-model-is-secretly-a-training-free","slug":"diffusion-model-is-secretly-a-training-free","title":"Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter","date":"2023-09-06","arxiv_id":"2309.02773","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-model-is-secretly-a-training-free#ran","syntology_url":"https://syntology.ai/paper/2309.02773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.02773"}},"official":{"repos":["VCG-team/DiffSegmenter"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-and-resource-efficient-object-tracking","slug":"fast-and-resource-efficient-object-tracking","title":"Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study","date":"2023-09-06","arxiv_id":"2309.02666","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/sparse-3d-reconstruction-via-object-centric","slug":"sparse-3d-reconstruction-via-object-centric","title":"Sparse 3D Reconstruction via Object-Centric Ray Sampling","date":"2023-09-06","arxiv_id":"2309.03008","repositories_listed":1,"syntology":null},{"url":"/paper/vote2cap-detr-decoupling-localization-and","slug":"vote2cap-detr-decoupling-localization-and","title":"Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning","date":"2023-09-06","arxiv_id":"2309.02999","repositories_listed":1,"syntology":null},{"url":"/paper/dr-pose-a-two-stage-deformation-and","slug":"dr-pose-a-two-stage-deformation-and","title":"DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation","date":"2023-09-05","arxiv_id":"2309.01925","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-superquadric-recomposition-of-3d","slug":"iterative-superquadric-recomposition-of-3d","title":"Iterative Superquadric Recomposition of 3D Objects from Multiple Views","date":"2023-09-05","arxiv_id":"2309.02102","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-grouping-with-transformer-for-1","slug":"contrastive-grouping-with-transformer-for-1","title":"Contrastive Grouping with Transformer for Referring Image Segmentation","date":"2023-09-02","arxiv_id":"2309.01017","repositories_listed":1,"syntology":{"n":23,"n_ran":22,"n_constructed":0,"n_ran_checked":13,"n_instrument":9,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":10,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 9 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-grouping-with-transformer-for-1#ran","syntology_url":"https://syntology.ai/paper/2309.01017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01017"}},"official":{"repos":["toneyaya/cgformer"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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/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/towards-addressing-the-misalignment-of-object","slug":"towards-addressing-the-misalignment-of-object","title":"Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic Grounding","date":"2023-09-01","arxiv_id":"2309.00215","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-to-fine-amodal-segmentation-with-shape","slug":"coarse-to-fine-amodal-segmentation-with-shape","title":"Coarse-to-Fine Amodal Segmentation with Shape Prior","date":"2023-08-31","arxiv_id":"2308.16825","repositories_listed":1,"syntology":null},{"url":"/paper/interdiff-generating-3d-human-object","slug":"interdiff-generating-3d-human-object","title":"InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion","date":"2023-08-31","arxiv_id":"2308.16905","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"6 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interdiff-generating-3d-human-object#ran","syntology_url":"https://syntology.ai/paper/2308.16905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.16905"}},"official":{"repos":["Sirui-Xu/InterDiff"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"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},{"url":"/paper/on-the-robustness-of-object-detection-models","slug":"on-the-robustness-of-object-detection-models","title":"On the Robustness of Object Detection Models on Aerial Images","date":"2023-08-29","arxiv_id":"2308.15378","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-cross-task-protocol-inconsistency","slug":"bridging-cross-task-protocol-inconsistency","title":"Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection","date":"2023-08-28","arxiv_id":"2308.14286","repositories_listed":1,"syntology":null}],"record_sha256":"66008006a79278210ea4fe7302ca1ae128facadff2197a351600283bdc72d81f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}