{"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/15","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":15,"pages_in_order":107,"rows_per_page":100,"rows":[1401,1500],"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/14","next":"/task/object/papers/16","papers":[{"url":"/paper/eagle-eigen-aggregation-learning-for-object","slug":"eagle-eigen-aggregation-learning-for-object","title":"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation","date":"2024-03-03","arxiv_id":"2403.01482","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/eagle-eigen-aggregation-learning-for-object#ran","syntology_url":"https://syntology.ai/paper/2403.01482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01482"}},"official":{"repos":["MICV-yonsei/EAGLE"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-yet-effective-network-based-on","slug":"a-simple-yet-effective-network-based-on","title":"A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection","date":"2024-02-29","arxiv_id":"2402.18922","repositories_listed":1,"syntology":null},{"url":"/paper/aligning-knowledge-graph-with-visual","slug":"aligning-knowledge-graph-with-visual","title":"Aligning Knowledge Graph with Visual Perception for Object-goal Navigation","date":"2024-02-29","arxiv_id":"2402.18892","repositories_listed":1,"syntology":null},{"url":"/paper/fusionvision-a-comprehensive-approach-of-3d","slug":"fusionvision-a-comprehensive-approach-of-3d","title":"FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything","date":"2024-02-29","arxiv_id":"2403.00175","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-autoencoder-for","slug":"privacy-preserving-autoencoder-for","title":"Privacy-Preserving Autoencoder for Collaborative Object Detection","date":"2024-02-29","arxiv_id":"2402.18864","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/privacy-preserving-autoencoder-for#ran","syntology_url":"https://syntology.ai/paper/2402.18864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18864"}},"official":{"repos":["bardia-az/ppa-code"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-multimodal-handover-failure-detection","slug":"a-multimodal-handover-failure-detection","title":"A Multimodal Handover Failure Detection Dataset and Baselines","date":"2024-02-28","arxiv_id":"2402.18319","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-micromobility-vehicles-in-urban","slug":"detection-of-micromobility-vehicles-in-urban","title":"Detection of Micromobility Vehicles in Urban Traffic Videos","date":"2024-02-28","arxiv_id":"2402.18503","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-aerial-object-detection-with-visual","slug":"zero-shot-aerial-object-detection-with-visual","title":"Zero-Shot Aerial Object Detection with Visual Description Regularization","date":"2024-02-28","arxiv_id":"2402.18233","repositories_listed":1,"syntology":null},{"url":"/paper/oscar-object-state-captioning-and-state","slug":"oscar-object-state-captioning-and-state","title":"OSCaR: Object State Captioning and State Change Representation","date":"2024-02-27","arxiv_id":"2402.17128","repositories_listed":1,"syntology":null},{"url":"/paper/hoisdf-constraining-3d-hand-object-pose","slug":"hoisdf-constraining-3d-hand-object-pose","title":"HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields","date":"2024-02-26","arxiv_id":"2402.17062","repositories_listed":1,"syntology":{"n":23,"n_ran":16,"n_constructed":2,"n_ran_checked":9,"n_instrument":7,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":23,"phrase":"16 ran (of which 2 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/hoisdf-constraining-3d-hand-object-pose#ran","syntology_url":"https://syntology.ai/paper/2402.17062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17062"}},"official":{"repos":["amathislab/hoisdf"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":2,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-open-world-object-detection","slug":"semi-supervised-open-world-object-detection","title":"Semi-supervised Open-World Object Detection","date":"2024-02-25","arxiv_id":"2402.16013","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-supervised-open-world-object-detection#ran","syntology_url":"https://syntology.ai/paper/2402.16013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16013"}},"official":{"repos":["sahalshajim/ss-owformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/grasp-see-and-place-efficient-unknown-object","slug":"grasp-see-and-place-efficient-unknown-object","title":"Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior","date":"2024-02-23","arxiv_id":"2402.15402","repositories_listed":1,"syntology":null},{"url":"/paper/transgop-transformer-based-gaze-object","slug":"transgop-transformer-based-gaze-object","title":"TransGOP: Transformer-Based Gaze Object Prediction","date":"2024-02-21","arxiv_id":"2402.13578","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/transgop-transformer-based-gaze-object#ran","syntology_url":"https://syntology.ai/paper/2402.13578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13578"}},"official":{"repos":["chenxi-guo/transgop"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/voom-robust-visual-object-odometry-and","slug":"voom-robust-visual-object-odometry-and","title":"VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks","date":"2024-02-21","arxiv_id":"2402.13609","repositories_listed":1,"syntology":null},{"url":"/paper/grafford-a-benchmark-dataset-for-testing-the","slug":"grafford-a-benchmark-dataset-for-testing-the","title":"TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text","date":"2024-02-20","arxiv_id":"2402.12881","repositories_listed":1,"syntology":null},{"url":"/paper/mulan-multimodal-llm-agent-for-progressive","slug":"mulan-multimodal-llm-agent-for-progressive","title":"MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion","date":"2024-02-20","arxiv_id":"2402.12741","repositories_listed":1,"syntology":null},{"url":"/paper/object-level-geometric-structure-preserving","slug":"object-level-geometric-structure-preserving","title":"Object-level Geometric Structure Preserving for Natural Image Stitching","date":"2024-02-20","arxiv_id":"2402.12677","repositories_listed":1,"syntology":null},{"url":"/paper/visual-reasoning-in-object-centric-deep","slug":"visual-reasoning-in-object-centric-deep","title":"Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition Approach","date":"2024-02-20","arxiv_id":"2402.12675","repositories_listed":1,"syntology":null},{"url":"/paper/open3dsg-open-vocabulary-3d-scene-graphs-from","slug":"open3dsg-open-vocabulary-3d-scene-graphs-from","title":"Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships","date":"2024-02-19","arxiv_id":"2402.12259","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/open3dsg-open-vocabulary-3d-scene-graphs-from#ran","syntology_url":"https://syntology.ai/paper/2402.12259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12259"}},"official":{"repos":["boschresearch/Open3DSG"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertaintytrack-exploiting-detection-and","slug":"uncertaintytrack-exploiting-detection-and","title":"UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking","date":"2024-02-19","arxiv_id":"2402.12303","repositories_listed":1,"syntology":null},{"url":"/paper/logical-closed-loop-uncovering-object","slug":"logical-closed-loop-uncovering-object","title":"Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models","date":"2024-02-18","arxiv_id":"2402.11622","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/logical-closed-loop-uncovering-object#ran","syntology_url":"https://syntology.ai/paper/2402.11622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11622"}},"official":{"repos":["hyperwjf/logiccheckgpt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/collavo-crayon-large-language-and-vision","slug":"collavo-crayon-large-language-and-vision","title":"CoLLaVO: Crayon Large Language and Vision mOdel","date":"2024-02-17","arxiv_id":"2402.11248","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/collavo-crayon-large-language-and-vision#ran","syntology_url":"https://syntology.ai/paper/2402.11248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11248"}},"official":{"repos":["ByungKwanLee/CoLLaVO"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lester-rotoscope-animation-through-video","slug":"lester-rotoscope-animation-through-video","title":"Lester: rotoscope animation through video object segmentation and tracking","date":"2024-02-15","arxiv_id":"2402.09883","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-anomalous-events-in-object-centric","slug":"detecting-anomalous-events-in-object-centric","title":"Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks","date":"2024-02-14","arxiv_id":"2403.00775","repositories_listed":1,"syntology":null},{"url":"/paper/h2o-sdf-two-phase-learning-for-3d-indoor","slug":"h2o-sdf-two-phase-learning-for-3d-indoor","title":"H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields","date":"2024-02-13","arxiv_id":"2402.08138","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-perceptual-limitation-of-multimodal","slug":"exploring-perceptual-limitation-of-multimodal","title":"Exploring Perceptual Limitation of Multimodal Large Language Models","date":"2024-02-12","arxiv_id":"2402.07384","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/exploring-perceptual-limitation-of-multimodal#ran","syntology_url":"https://syntology.ai/paper/2402.07384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07384"}},"official":{"repos":["saccharomycetes/mllm-perceptual-limitation"],"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/gbot-graph-based-3d-object-tracking-for","slug":"gbot-graph-based-3d-object-tracking-for","title":"GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance","date":"2024-02-12","arxiv_id":"2402.07677","repositories_listed":1,"syntology":null},{"url":"/paper/star-shape-focused-texture-agnostic","slug":"star-shape-focused-texture-agnostic","title":"Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation","date":"2024-02-07","arxiv_id":"2402.04878","repositories_listed":1,"syntology":null},{"url":"/paper/yolopoint-joint-keypoint-and-object-detection","slug":"yolopoint-joint-keypoint-and-object-detection","title":"YOLOPoint Joint Keypoint and Object Detection","date":"2024-02-06","arxiv_id":"2402.03989","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-two-view-geometry-from-object-poses","slug":"extreme-two-view-geometry-from-object-poses","title":"Extreme Two-View Geometry From Object Poses with Diffusion Models","date":"2024-02-05","arxiv_id":"2402.02800","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":1,"n_no_contract":4,"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, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/extreme-two-view-geometry-from-object-poses#ran","syntology_url":"https://syntology.ai/paper/2402.02800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02800"}},"official":{"repos":["scy639/extreme-two-view-geometry-from-object-poses-with-diffusion-models"],"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/hassod-hierarchical-adaptive-self-supervised-1","slug":"hassod-hierarchical-adaptive-self-supervised-1","title":"HASSOD: Hierarchical Adaptive Self-Supervised Object Detection","date":"2024-02-05","arxiv_id":"2402.03311","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hassod-hierarchical-adaptive-self-supervised-1#ran","syntology_url":"https://syntology.ai/paper/2402.03311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03311"}},"official":{"repos":["shengcao-cao/hassod"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sgs-slam-semantic-gaussian-splatting-for","slug":"sgs-slam-semantic-gaussian-splatting-for","title":"SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM","date":"2024-02-05","arxiv_id":"2402.03246","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/sgs-slam-semantic-gaussian-splatting-for#ran","syntology_url":"https://syntology.ai/paper/2402.03246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03246"}},"official":{"repos":["shuhongll/sgs-slam"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/noah-learning-pairwise-object-category","slug":"noah-learning-pairwise-object-category","title":"NOAH: Learning Pairwise Object Category Attentions for Image Classification","date":"2024-02-04","arxiv_id":"2402.02377","repositories_listed":1,"syntology":null},{"url":"/paper/hyperplanes-hypernetwork-approach-to-rapid","slug":"hyperplanes-hypernetwork-approach-to-rapid","title":"HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation","date":"2024-02-02","arxiv_id":"2402.01524","repositories_listed":1,"syntology":null},{"url":"/paper/mod-cl-multi-label-object-detection-with","slug":"mod-cl-multi-label-object-detection-with","title":"MOD-CL: Multi-label Object Detection with Constrained Loss","date":"2024-01-31","arxiv_id":"2403.07885","repositories_listed":1,"syntology":null},{"url":"/paper/mf-mos-a-motion-focused-model-for-moving","slug":"mf-mos-a-motion-focused-model-for-moving","title":"MF-MOS: A Motion-Focused Model for Moving Object Segmentation","date":"2024-01-30","arxiv_id":"2401.17023","repositories_listed":1,"syntology":null},{"url":"/paper/hand-centric-motion-refinement-for-3d-hand","slug":"hand-centric-motion-refinement-for-3d-hand","title":"Hand-Centric Motion Refinement for 3D Hand-Object Interaction via Hierarchical Spatial-Temporal Modeling","date":"2024-01-29","arxiv_id":"2401.15987","repositories_listed":1,"syntology":null},{"url":"/paper/textual-entailment-for-effective-triple","slug":"textual-entailment-for-effective-triple","title":"Textual Entailment for Effective Triple Validation in Object Prediction","date":"2024-01-29","arxiv_id":"2401.16293","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-for-the-primitives-of-3d","slug":"zero-shot-learning-for-the-primitives-of-3d","title":"Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models","date":"2024-01-23","arxiv_id":"2401.12978","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/zero-shot-learning-for-the-primitives-of-3d#ran","syntology_url":"https://syntology.ai/paper/2401.12978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12978"}},"official":{"repos":["snuvclab/coma"],"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/semples-semantic-prompt-learning-for-weakly","slug":"semples-semantic-prompt-learning-for-weakly","title":"Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation","date":"2024-01-22","arxiv_id":"2401.11791","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-structure-constraints-for-weakly","slug":"spatial-structure-constraints-for-weakly","title":"Spatial Structure Constraints for Weakly Supervised Semantic Segmentation","date":"2024-01-20","arxiv_id":"2401.11122","repositories_listed":1,"syntology":null},{"url":"/paper/vonet-unsupervised-video-object-learning-with","slug":"vonet-unsupervised-video-object-learning-with","title":"VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE","date":"2024-01-20","arxiv_id":"2401.11110","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vonet-unsupervised-video-object-learning-with#ran","syntology_url":"https://syntology.ai/paper/2401.11110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11110"}},"official":{"repos":["hnyu/vonet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/focaler-iou-more-focused-intersection-over","slug":"focaler-iou-more-focused-intersection-over","title":"Focaler-IoU: More Focused Intersection over Union Loss","date":"2024-01-19","arxiv_id":"2401.10525","repositories_listed":1,"syntology":null},{"url":"/paper/nwpu-moc-a-benchmark-for-fine-grained-multi","slug":"nwpu-moc-a-benchmark-for-fine-grained-multi","title":"NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images","date":"2024-01-19","arxiv_id":"2401.10530","repositories_listed":1,"syntology":null},{"url":"/paper/removal-and-selection-improving-rgb-infrared","slug":"removal-and-selection-improving-rgb-infrared","title":"Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection","date":"2024-01-19","arxiv_id":"2401.10731","repositories_listed":1,"syntology":null},{"url":"/paper/blenda-domain-adaptive-object-detection","slug":"blenda-domain-adaptive-object-detection","title":"BlenDA: Domain Adaptive Object Detection through diffusion-based blending","date":"2024-01-18","arxiv_id":"2401.09921","repositories_listed":1,"syntology":null},{"url":"/paper/explicitly-disentangled-representations-in","slug":"explicitly-disentangled-representations-in","title":"Explicitly Disentangled Representations in Object-Centric Learning","date":"2024-01-18","arxiv_id":"2401.10148","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":8,"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/explicitly-disentangled-representations-in#ran","syntology_url":"https://syntology.ai/paper/2401.10148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10148"}},"official":{"repos":["riccardomajellaro/disentangled-slot-attention"],"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/icgnet-a-unified-approach-for-instance","slug":"icgnet-a-unified-approach-for-instance","title":"ICGNet: A Unified Approach for Instance-Centric Grasping","date":"2024-01-18","arxiv_id":"2401.09939","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/icgnet-a-unified-approach-for-instance#ran","syntology_url":"https://syntology.ai/paper/2401.09939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09939"}},"official":{"repos":["renezurbruegg/icg_benchmark"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/completely-occluded-and-dense-object-instance","slug":"completely-occluded-and-dense-object-instance","title":"OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts","date":"2024-01-16","arxiv_id":"2401.08174","repositories_listed":1,"syntology":null},{"url":"/paper/fast-dynamic-3d-object-generation-from-a","slug":"fast-dynamic-3d-object-generation-from-a","title":"Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video","date":"2024-01-16","arxiv_id":"2401.08742","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"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) · 0 unverified","sample_list":"/paper/fast-dynamic-3d-object-generation-from-a#ran","syntology_url":"https://syntology.ai/paper/2401.08742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08742"}},"official":{"repos":["fudan-zvg/efficient4d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-implicit-representation-for","slug":"learning-implicit-representation-for","title":"Learning Implicit Representation for Reconstructing Articulated Objects","date":"2024-01-16","arxiv_id":"2401.08809","repositories_listed":1,"syntology":null},{"url":"/paper/robust-tiny-object-detection-in-aerial-images","slug":"robust-tiny-object-detection-in-aerial-images","title":"Robust Tiny Object Detection in Aerial Images amidst Label Noise","date":"2024-01-16","arxiv_id":"2401.08056","repositories_listed":1,"syntology":null},{"url":"/paper/cascadev-det-cascade-point-voting-for-3d","slug":"cascadev-det-cascade-point-voting-for-3d","title":"CascadeV-Det: Cascade Point Voting for 3D Object Detection","date":"2024-01-15","arxiv_id":"2401.07477","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-prototypes-distillation-for-few","slug":"fine-grained-prototypes-distillation-for-few","title":"Fine-Grained Prototypes Distillation for Few-Shot Object Detection","date":"2024-01-15","arxiv_id":"2401.07629","repositories_listed":1,"syntology":null},{"url":"/paper/dcdet-dynamic-cross-based-3d-object-detector","slug":"dcdet-dynamic-cross-based-3d-object-detector","title":"DCDet: Dynamic Cross-based 3D Object Detector","date":"2024-01-14","arxiv_id":"2401.07240","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"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) · 4 unverified","sample_list":"/paper/dcdet-dynamic-cross-based-3d-object-detector#ran","syntology_url":"https://syntology.ai/paper/2401.07240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07240"}},"official":{"repos":["say2l/dcdet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rsud20k-a-dataset-for-road-scene","slug":"rsud20k-a-dataset-for-road-scene","title":"RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving","date":"2024-01-14","arxiv_id":"2401.07322","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/rsud20k-a-dataset-for-road-scene#ran","syntology_url":"https://syntology.ai/paper/2401.07322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07322"}},"official":{"repos":["hasibzunair/rsud20k"],"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/transformer-for-object-re-identification-a","slug":"transformer-for-object-re-identification-a","title":"Transformer for Object Re-Identification: A Survey","date":"2024-01-13","arxiv_id":"2401.06960","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transformer-for-object-re-identification-a#ran","syntology_url":"https://syntology.ai/paper/2401.06960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06960"}},"official":{"repos":["mangye16/ReID-Survey"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/clip-guided-source-free-object-detection-in","slug":"clip-guided-source-free-object-detection-in","title":"CLIP-Guided Source-Free Object Detection in Aerial Images","date":"2024-01-10","arxiv_id":"2401.05168","repositories_listed":1,"syntology":null},{"url":"/paper/consensus-focus-for-object-detection-and","slug":"consensus-focus-for-object-detection-and","title":"Consensus Focus for Object Detection and minority classes","date":"2024-01-10","arxiv_id":"2401.05530","repositories_listed":1,"syntology":null},{"url":"/paper/structure-from-duplicates-neural-inverse-1","slug":"structure-from-duplicates-neural-inverse-1","title":"Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects","date":"2024-01-10","arxiv_id":"2401.05236","repositories_listed":1,"syntology":null},{"url":"/paper/flying-bird-object-detection-algorithm-in","slug":"flying-bird-object-detection-algorithm-in","title":"A Flying Bird Object Detection Method for Surveillance Video","date":"2024-01-08","arxiv_id":"2401.03749","repositories_listed":1,"syntology":null},{"url":"/paper/ms-detr-efficient-detr-training-with-mixed","slug":"ms-detr-efficient-detr-training-with-mixed","title":"MS-DETR: Efficient DETR Training with Mixed Supervision","date":"2024-01-08","arxiv_id":"2401.03989","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ms-detr-efficient-detr-training-with-mixed#ran","syntology_url":"https://syntology.ai/paper/2401.03989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03989"}},"official":{"repos":["atten4vis/ms-detr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robofusion-towards-robust-multi-modal-3d","slug":"robofusion-towards-robust-multi-modal-3d","title":"RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM","date":"2024-01-08","arxiv_id":"2401.03907","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/robofusion-towards-robust-multi-modal-3d#ran","syntology_url":"https://syntology.ai/paper/2401.03907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03907"}},"official":{"repos":["adept-thu/RoboFusion"],"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/distformer-enhancing-local-and-global","slug":"distformer-enhancing-local-and-global","title":"DistFormer: Enhancing Local and Global Features for Monocular Per-Object Distance Estimation","date":"2024-01-06","arxiv_id":"2401.03191","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/distformer-enhancing-local-and-global#ran","syntology_url":"https://syntology.ai/paper/2401.03191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03191"}},"official":{"repos":["apanariello4/DistFormer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/explicit-visual-prompts-for-visual-object","slug":"explicit-visual-prompts-for-visual-object","title":"Explicit Visual Prompts for Visual Object Tracking","date":"2024-01-06","arxiv_id":"2401.03142","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/explicit-visual-prompts-for-visual-object#ran","syntology_url":"https://syntology.ai/paper/2401.03142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03142"}},"official":{"repos":["GXNU-ZhongLab/EVPTrack"],"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/clustering-guided-class-activation-for-weakly","slug":"clustering-guided-class-activation-for-weakly","title":"Clustering-Guided Class Activation for Weakly Supervised Semantic Segmentation","date":"2024-01-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pegasus-physically-enhanced-gaussian","slug":"pegasus-physically-enhanced-gaussian","title":"PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation","date":"2024-01-04","arxiv_id":"2401.02281","repositories_listed":1,"syntology":null},{"url":"/paper/1st-place-solution-for-5th-lsvos-challenge","slug":"1st-place-solution-for-5th-lsvos-challenge","title":"1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation","date":"2024-01-01","arxiv_id":"2401.00663","repositories_listed":1,"syntology":null},{"url":"/paper/cn-rma-combined-network-with-ray-marching-1","slug":"cn-rma-combined-network-with-ray-marching-1","title":"CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoor Object Detection from Multi-view Images","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/diod-self-distillation-meets-object-discovery","slug":"diod-self-distillation-meets-object-discovery","title":"DIOD: Self-Distillation Meets Object Discovery","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exploring-orthogonality-in-open-world-object","slug":"exploring-orthogonality-in-open-world-object","title":"Exploring Orthogonality in Open World Object Detection","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/laso-language-guided-affordance-segmentation","slug":"laso-language-guided-affordance-segmentation","title":"LASO: Language-guided Affordance Segmentation on 3D Object","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pair-diffusion-a-comprehensive-multimodal","slug":"pair-diffusion-a-comprehensive-multimodal","title":"PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pairdetr-joint-detection-and-association-of","slug":"pairdetr-joint-detection-and-association-of","title":"PairDETR : Joint Detection and Association of Human Bodies and Faces","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/person-in-place-generating-associative","slug":"person-in-place-generating-associative","title":"Person in Place: Generating Associative Skeleton-Guidance Maps for Human-Object Interaction Image Editing","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/point-segment-and-count-a-generalized-1","slug":"point-segment-and-count-a-generalized-1","title":"Point Segment and Count: A Generalized Framework for Object Counting","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/shapematcher-self-supervised-joint-shape","slug":"shapematcher-self-supervised-joint-shape","title":"ShapeMatcher: Self-Supervised Joint Shape Canonicalization Segmentation Retrieval and Deformation","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tune-an-ellipse-clip-has-potential-to-find","slug":"tune-an-ellipse-clip-has-potential-to-find","title":"Tune-An-Ellipse: CLIP Has Potential to Find What You Want","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-parts-beyond-objects-towards-finer-1","slug":"unveiling-parts-beyond-objects-towards-finer-1","title":"Unveiling Parts Beyond Objects: Towards Finer-Granularity Referring Expression Segmentation","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generating-enhanced-negatives-for-training","slug":"generating-enhanced-negatives-for-training","title":"Generating Enhanced Negatives for Training Language-Based Object Detectors","date":"2023-12-29","arxiv_id":"2401.00094","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-with-human-intent-reasoning","slug":"tracking-with-human-intent-reasoning","title":"Tracking with Human-Intent Reasoning","date":"2023-12-29","arxiv_id":"2312.17448","repositories_listed":1,"syntology":null},{"url":"/paper/artrackv2-prompting-autoregressive-tracker","slug":"artrackv2-prompting-autoregressive-tracker","title":"ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe","date":"2023-12-28","arxiv_id":"2312.17133","repositories_listed":1,"syntology":null},{"url":"/paper/ifusion-inverting-diffusion-for-pose-free","slug":"ifusion-inverting-diffusion-for-pose-free","title":"iFusion: Inverting Diffusion for Pose-Free Reconstruction from Sparse Views","date":"2023-12-28","arxiv_id":"2312.17250","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":1,"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, 2 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ifusion-inverting-diffusion-for-pose-free#ran","syntology_url":"https://syntology.ai/paper/2312.17250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17250"}},"official":{"repos":["chinhsuanwu/ifusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/constscene-dataset-and-model-for-advancing","slug":"constscene-dataset-and-model-for-advancing","title":"ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments","date":"2023-12-27","arxiv_id":"2312.16516","repositories_listed":1,"syntology":null},{"url":"/paper/prototype-based-cross-modal-object-tracking","slug":"prototype-based-cross-modal-object-tracking","title":"Prototype-based Cross-Modal Object Tracking","date":"2023-12-22","arxiv_id":"2312.14471","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-few-shot-object-detection-with","slug":"revisiting-few-shot-object-detection-with","title":"Revisiting Few-Shot Object Detection with Vision-Language Models","date":"2023-12-22","arxiv_id":"2312.14494","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/revisiting-few-shot-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2312.14494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14494"}},"official":{"repos":["anishmadan23/foundational_fsod"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/universal-noise-annotation-unveiling-the","slug":"universal-noise-annotation-unveiling-the","title":"Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection","date":"2023-12-21","arxiv_id":"2312.13822","repositories_listed":1,"syntology":null},{"url":"/paper/vcoder-versatile-vision-encoders-for","slug":"vcoder-versatile-vision-encoders-for","title":"VCoder: Versatile Vision Encoders for Multimodal Large Language Models","date":"2023-12-21","arxiv_id":"2312.14233","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/vcoder-versatile-vision-encoders-for#ran","syntology_url":"https://syntology.ai/paper/2312.14233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14233"}},"official":{"repos":["shi-labs/vcoder"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/object-attribute-matters-in-visual-question","slug":"object-attribute-matters-in-visual-question","title":"Object Attribute Matters in Visual Question Answering","date":"2023-12-20","arxiv_id":"2401.09442","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-adaptive-positivity-learning-for","slug":"object-aware-adaptive-positivity-learning-for","title":"Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering","date":"2023-12-20","arxiv_id":"2312.12816","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/object-aware-adaptive-positivity-learning-for#ran","syntology_url":"https://syntology.ai/paper/2312.12816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12816"}},"official":{"repos":["zhangbin-ai/apl"],"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/earthvqa-towards-queryable-earth-via","slug":"earthvqa-towards-queryable-earth-via","title":"EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering","date":"2023-12-19","arxiv_id":"2312.12222","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/earthvqa-towards-queryable-earth-via#ran","syntology_url":"https://syntology.ai/paper/2312.12222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12222"}},"official":{"repos":["Junjue-Wang/EarthVQA"],"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/object-aware-domain-generalization-for-object","slug":"object-aware-domain-generalization-for-object","title":"Object-Aware Domain Generalization for Object Detection","date":"2023-12-19","arxiv_id":"2312.12133","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-any-object-amodally","slug":"tracking-any-object-amodally","title":"TAO-Amodal: A Benchmark for Tracking Any Object Amodally","date":"2023-12-19","arxiv_id":"2312.12433","repositories_listed":1,"syntology":null},{"url":"/paper/clim-contrastive-language-image-mosaic-for","slug":"clim-contrastive-language-image-mosaic-for","title":"CLIM: Contrastive Language-Image Mosaic for Region Representation","date":"2023-12-18","arxiv_id":"2312.11376","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clim-contrastive-language-image-mosaic-for#ran","syntology_url":"https://syntology.ai/paper/2312.11376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11376"}},"official":{"repos":["wusize/clim"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/petdet-proposal-enhancement-for-two-stage","slug":"petdet-proposal-enhancement-for-two-stage","title":"PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection","date":"2023-12-16","arxiv_id":"2312.10515","repositories_listed":1,"syntology":null},{"url":"/paper/simple-image-level-classification-improves","slug":"simple-image-level-classification-improves","title":"Simple Image-level Classification Improves Open-vocabulary Object Detection","date":"2023-12-16","arxiv_id":"2312.10439","repositories_listed":1,"syntology":null},{"url":"/paper/ins-hoi-instance-aware-human-object","slug":"ins-hoi-instance-aware-human-object","title":"Ins-HOI: Instance Aware Human-Object Interactions Recovery","date":"2023-12-15","arxiv_id":"2312.09641","repositories_listed":1,"syntology":null},{"url":"/paper/painterly-image-harmonization-by-learning","slug":"painterly-image-harmonization-by-learning","title":"Painterly Image Harmonization by Learning from Painterly Objects","date":"2023-12-15","arxiv_id":"2312.10263","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-knowledge-distillation-framework-for","slug":"a-simple-knowledge-distillation-framework-for","title":"SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector","date":"2023-12-14","arxiv_id":"2312.08653","repositories_listed":1,"syntology":null},{"url":"/paper/general-object-foundation-model-for-images","slug":"general-object-foundation-model-for-images","title":"General Object Foundation Model for Images and Videos at Scale","date":"2023-12-14","arxiv_id":"2312.09158","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"11 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/general-object-foundation-model-for-images#ran","syntology_url":"https://syntology.ai/paper/2312.09158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09158"}},"official":{"repos":["FoundationVision/GLEE"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"c0bbd846dac2c3d706744f20113c167735ab912c33519ba73c42fd2d1069fda0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}