{"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/10","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":10,"pages_in_order":107,"rows_per_page":100,"rows":[901,1000],"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/9","next":"/task/object/papers/11","papers":[{"url":"/paper/cross-domain-few-shot-object-detection-with","slug":"cross-domain-few-shot-object-detection-with","title":"Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment","date":"2025-02-23","arxiv_id":"2502.16469","repositories_listed":1,"syntology":null},{"url":"/paper/crossover-3d-scene-cross-modal-alignment","slug":"crossover-3d-scene-cross-modal-alignment","title":"CrossOver: 3D Scene Cross-Modal Alignment","date":"2025-02-20","arxiv_id":"2502.15011","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-image-to-video-generation-with","slug":"object-centric-image-to-video-generation-with","title":"Object-Centric Image to Video Generation with Language Guidance","date":"2025-02-17","arxiv_id":"2502.11655","repositories_listed":1,"syntology":null},{"url":"/paper/knowing-your-target-target-aware-transformer","slug":"knowing-your-target-target-aware-transformer","title":"Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding","date":"2025-02-16","arxiv_id":"2502.11168","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-and-tracking","slug":"object-detection-and-tracking","title":"Object Detection and Tracking","date":"2025-02-14","arxiv_id":"2502.10310","repositories_listed":1,"syntology":null},{"url":"/paper/playslot-learning-inverse-latent-dynamics-for","slug":"playslot-learning-inverse-latent-dynamics-for","title":"PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning","date":"2025-02-11","arxiv_id":"2502.07600","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"2 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/playslot-learning-inverse-latent-dynamics-for#ran","syntology_url":"https://syntology.ai/paper/2502.07600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.07600"}},"official":null}},{"url":"/paper/save-self-attention-on-visual-embedding-for","slug":"save-self-attention-on-visual-embedding-for","title":"SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting","date":"2025-02-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/diff9d-diffusion-based-domain-generalized","slug":"diff9d-diffusion-based-domain-generalized","title":"Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation","date":"2025-02-04","arxiv_id":"2502.02525","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-vision-transformer-for-object","slug":"rethinking-vision-transformer-for-object","title":"Rethinking Vision Transformer for Object Centric Foundation Models","date":"2025-02-04","arxiv_id":"2502.02763","repositories_listed":1,"syntology":null},{"url":"/paper/tumtraffic-videoqa-a-benchmark-for-unified","slug":"tumtraffic-videoqa-a-benchmark-for-unified","title":"TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes","date":"2025-02-04","arxiv_id":"2502.02449","repositories_listed":1,"syntology":null},{"url":"/paper/spikingrtnh-spiking-neural-network-for-4d","slug":"spikingrtnh-spiking-neural-network-for-4d","title":"SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection","date":"2025-01-31","arxiv_id":"2502.00074","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-feature-fusion-for-uav-object","slug":"efficient-feature-fusion-for-uav-object","title":"Efficient Feature Fusion for UAV Object Detection","date":"2025-01-29","arxiv_id":"2501.17983","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-and-boosting-the-power-of-fine","slug":"analyzing-and-boosting-the-power-of-fine","title":"Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models","date":"2025-01-25","arxiv_id":"2501.15140","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/analyzing-and-boosting-the-power-of-fine#ran","syntology_url":"https://syntology.ai/paper/2501.15140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.15140"}},"official":{"repos":["pku-icst-mipl/finedefics_iclr2025"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/smamba-sparse-mamba-for-event-based-object","slug":"smamba-sparse-mamba-for-event-based-object","title":"SMamba: Sparse Mamba for Event-based Object Detection","date":"2025-01-21","arxiv_id":"2501.11971","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/smamba-sparse-mamba-for-event-based-object#ran","syntology_url":"https://syntology.ai/paper/2501.11971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.11971"}},"official":{"repos":["Zizzzzzzz/SMamba_AAAI2025"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/surface-sos-self-supervised-object","slug":"surface-sos-self-supervised-object","title":"Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation","date":"2025-01-17","arxiv_id":"2501.09947","repositories_listed":1,"syntology":null},{"url":"/paper/everybody-likes-to-sleep-a-computer-assisted","slug":"everybody-likes-to-sleep-a-computer-assisted","title":"Everybody Likes to Sleep: A Computer-Assisted Comparison of Object Naming Data from 30 Languages","date":"2025-01-14","arxiv_id":"2501.08312","repositories_listed":1,"syntology":null},{"url":"/paper/learning-motion-and-temporal-cues-for","slug":"learning-motion-and-temporal-cues-for","title":"Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation","date":"2025-01-14","arxiv_id":"2501.07806","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-performance-of-object-detection","slug":"predicting-performance-of-object-detection","title":"Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests","date":"2025-01-14","arxiv_id":"2501.08465","repositories_listed":1,"syntology":null},{"url":"/paper/sst-em-advanced-metrics-for-evaluating","slug":"sst-em-advanced-metrics-for-evaluating","title":"SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing","date":"2025-01-13","arxiv_id":"2501.07554","repositories_listed":1,"syntology":null},{"url":"/paper/toward-realistic-camouflaged-object-detection","slug":"toward-realistic-camouflaged-object-detection","title":"Toward Realistic Camouflaged Object Detection: Benchmarks and Method","date":"2025-01-13","arxiv_id":"2501.07297","repositories_listed":1,"syntology":null},{"url":"/paper/uncommon-objects-in-3d","slug":"uncommon-objects-in-3d","title":"UnCommon Objects in 3D","date":"2025-01-13","arxiv_id":"2501.07574","repositories_listed":1,"syntology":null},{"url":"/paper/3dcompat200-language-grounded-compositional","slug":"3dcompat200-language-grounded-compositional","title":"3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes","date":"2025-01-12","arxiv_id":"2501.06785","repositories_listed":1,"syntology":null},{"url":"/paper/improving-skeleton-based-action-recognition","slug":"improving-skeleton-based-action-recognition","title":"Improving Skeleton-based Action Recognition with Interactive Object Information","date":"2025-01-09","arxiv_id":"2501.05066","repositories_listed":1,"syntology":null},{"url":"/paper/sa2va-marrying-sam2-with-llava-for-dense","slug":"sa2va-marrying-sam2-with-llava-for-dense","title":"Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos","date":"2025-01-07","arxiv_id":"2501.04001","repositories_listed":1,"syntology":null},{"url":"/paper/texhoi-reconstructing-textures-of-3d-unknown","slug":"texhoi-reconstructing-textures-of-3d-unknown","title":"TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes","date":"2025-01-07","arxiv_id":"2501.03525","repositories_listed":1,"syntology":null},{"url":"/paper/universal-fine-grained-visual-categorization","slug":"universal-fine-grained-visual-categorization","title":"Universal Fine-grained Visual Categorization by Concept Guided Learning","date":"2025-01-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalization-enhanced-few-shot-object","slug":"generalization-enhanced-few-shot-object","title":"Generalization-Enhanced Few-Shot Object Detection in Remote Sensing","date":"2025-01-05","arxiv_id":"2501.02474","repositories_listed":1,"syntology":null},{"url":"/paper/common3d-self-supervised-learning-of-3d","slug":"common3d-self-supervised-learning-of-3d","title":"Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fusionsort-fusion-methods-for-online-multi","slug":"fusionsort-fusion-methods-for-online-multi","title":"FusionSORT: Fusion Methods for Online Multi-object Visual Tracking","date":"2025-01-01","arxiv_id":"2501.00843","repositories_listed":1,"syntology":null},{"url":"/paper/go-n3rdet-geometry-optimized-nerf-enhanced-3d","slug":"go-n3rdet-geometry-optimized-nerf-enhanced-3d","title":"GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-partonomic-3d-reconstruction-from","slug":"learning-partonomic-3d-reconstruction-from","title":"Learning Partonomic 3D Reconstruction from Image Collections","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-3d-object-canonicalization-based-on","slug":"one-shot-3d-object-canonicalization-based-on","title":"One-shot 3D Object Canonicalization based on Geometric and Semantic Consistency","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/prior-free-3d-object-tracking","slug":"prior-free-3d-object-tracking","title":"Prior-free 3D Object Tracking","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rorem-training-a-robust-object-remover-with","slug":"rorem-training-a-robust-object-remover-with","title":"RORem: Training a Robust Object Remover with Human-in-the-Loop","date":"2025-01-01","arxiv_id":"2501.00740","repositories_listed":1,"syntology":null},{"url":"/paper/videorefer-suite-advancing-spatial-temporal","slug":"videorefer-suite-advancing-spatial-temporal","title":"VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM","date":"2024-12-31","arxiv_id":"2501.00599","repositories_listed":1,"syntology":null},{"url":"/paper/yolo-uniow-efficient-universal-open-world","slug":"yolo-uniow-efficient-universal-open-world","title":"YOLO-UniOW: Efficient Universal Open-World Object Detection","date":"2024-12-30","arxiv_id":"2412.20645","repositories_listed":1,"syntology":null},{"url":"/paper/hallucinogen-a-benchmark-for-evaluating","slug":"hallucinogen-a-benchmark-for-evaluating","title":"HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models","date":"2024-12-29","arxiv_id":"2412.20622","repositories_listed":1,"syntology":null},{"url":"/paper/driveeditor-a-unified-3d-information-guided","slug":"driveeditor-a-unified-3d-information-guided","title":"DriveEditor: A Unified 3D Information-Guided Framework for Controllable Object Editing in Driving Scenes","date":"2024-12-27","arxiv_id":"2412.19458","repositories_listed":1,"syntology":null},{"url":"/paper/interacted-object-grounding-in-spatio","slug":"interacted-object-grounding-in-spatio","title":"Interacted Object Grounding in Spatio-Temporal Human-Object Interactions","date":"2024-12-27","arxiv_id":"2412.19542","repositories_listed":1,"syntology":null},{"url":"/paper/cgcod-class-guided-camouflaged-object","slug":"cgcod-class-guided-camouflaged-object","title":"CGCOD: Class-Guided Camouflaged Object Detection","date":"2024-12-25","arxiv_id":"2412.18977","repositories_listed":1,"syntology":null},{"url":"/paper/distortion-aware-adversarial-attacks-on","slug":"distortion-aware-adversarial-attacks-on","title":"Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors","date":"2024-12-25","arxiv_id":"2412.18815","repositories_listed":1,"syntology":null},{"url":"/paper/cross-view-referring-multi-object-tracking","slug":"cross-view-referring-multi-object-tracking","title":"Cross-View Referring Multi-Object Tracking","date":"2024-12-23","arxiv_id":"2412.17807","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/cross-view-referring-multi-object-tracking#ran","syntology_url":"https://syntology.ai/paper/2412.17807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.17807"}},"official":{"repos":["chen-si-jia/crmot"],"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/s-inf-towards-realistic-indoor-scene","slug":"s-inf-towards-realistic-indoor-scene","title":"S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit Neural Field","date":"2024-12-23","arxiv_id":"2412.17561","repositories_listed":1,"syntology":null},{"url":"/paper/seamless-detection-unifying-salient-object","slug":"seamless-detection-unifying-salient-object","title":"Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection","date":"2024-12-22","arxiv_id":"2412.16840","repositories_listed":1,"syntology":null},{"url":"/paper/improving-object-detection-for-time-lapse","slug":"improving-object-detection-for-time-lapse","title":"Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring","date":"2024-12-20","arxiv_id":"2412.16329","repositories_listed":1,"syntology":null},{"url":"/paper/affordance-aware-object-insertion-via-mask","slug":"affordance-aware-object-insertion-via-mask","title":"Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion","date":"2024-12-19","arxiv_id":"2412.14462","repositories_listed":1,"syntology":null},{"url":"/paper/multi-sensor-object-anomaly-detection","slug":"multi-sensor-object-anomaly-detection","title":"Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties","date":"2024-12-19","arxiv_id":"2412.14592","repositories_listed":1,"syntology":null},{"url":"/paper/descriptive-caption-enhancement-with-visual","slug":"descriptive-caption-enhancement-with-visual","title":"Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception","date":"2024-12-18","arxiv_id":"2412.14233","repositories_listed":1,"syntology":null},{"url":"/paper/m-3-vos-multi-phase-multi-transition-and","slug":"m-3-vos-multi-phase-multi-transition-and","title":"M$^3$-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation","date":"2024-12-18","arxiv_id":"2412.13803","repositories_listed":1,"syntology":null},{"url":"/paper/pixelman-consistent-object-editing-with","slug":"pixelman-consistent-object-editing-with","title":"PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation","date":"2024-12-18","arxiv_id":"2412.14283","repositories_listed":1,"syntology":null},{"url":"/paper/relationfield-relate-anything-in-radiance","slug":"relationfield-relate-anything-in-radiance","title":"RelationField: Relate Anything in Radiance Fields","date":"2024-12-18","arxiv_id":"2412.13652","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-eraser-unleashing-diffusion-model-s","slug":"attentive-eraser-unleashing-diffusion-model-s","title":"Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential via Self-Attention Redirection Guidance","date":"2024-12-17","arxiv_id":"2412.12974","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/attentive-eraser-unleashing-diffusion-model-s#ran","syntology_url":"https://syntology.ai/paper/2412.12974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12974"}},"official":{"repos":["anonym0u3/attentiveeraser"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/crest-an-efficient-conjointly-trained-spike","slug":"crest-an-efficient-conjointly-trained-spike","title":"CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics","date":"2024-12-17","arxiv_id":"2412.12525","repositories_listed":1,"syntology":null},{"url":"/paper/differential-alignment-for-domain-adaptive","slug":"differential-alignment-for-domain-adaptive","title":"Differential Alignment for Domain Adaptive Object Detection","date":"2024-12-17","arxiv_id":"2412.12830","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-gospa-a-metric-for-performance","slug":"probabilistic-gospa-a-metric-for-performance","title":"Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties","date":"2024-12-16","arxiv_id":"2412.11482","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-enhanced-contextual-information-for-1","slug":"exploring-enhanced-contextual-information-for-1","title":"Exploring Enhanced Contextual Information for Video-Level Object Tracking","date":"2024-12-15","arxiv_id":"2412.11023","repositories_listed":1,"syntology":null},{"url":"/paper/redefining-normal-a-novel-object-level","slug":"redefining-normal-a-novel-object-level","title":"Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detection","date":"2024-12-15","arxiv_id":"2412.11148","repositories_listed":1,"syntology":null},{"url":"/paper/demo-decoupled-feature-based-mixture-of","slug":"demo-decoupled-feature-based-mixture-of","title":"DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification","date":"2024-12-14","arxiv_id":"2412.10650","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":3,"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/demo-decoupled-feature-based-mixture-of#ran","syntology_url":"https://syntology.ai/paper/2412.10650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.10650"}},"official":{"repos":["924973292/demo"],"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/heterogeneous-graph-transformer-for-multiple","slug":"heterogeneous-graph-transformer-for-multiple","title":"Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos","date":"2024-12-14","arxiv_id":"2412.10861","repositories_listed":1,"syntology":null},{"url":"/paper/mambapro-multi-modal-object-re-identification","slug":"mambapro-multi-modal-object-re-identification","title":"MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt","date":"2024-12-14","arxiv_id":"2412.10707","repositories_listed":1,"syntology":null},{"url":"/paper/remdet-rethinking-efficient-model-design-for","slug":"remdet-rethinking-efficient-model-design-for","title":"RemDet: Rethinking Efficient Model Design for UAV Object Detection","date":"2024-12-13","arxiv_id":"2412.10040","repositories_listed":1,"syntology":null},{"url":"/paper/dali-domain-adaptive-lidar-object-detection","slug":"dali-domain-adaptive-lidar-object-detection","title":"DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label Denoising","date":"2024-12-11","arxiv_id":"2412.08806","repositories_listed":1,"syntology":null},{"url":"/paper/no-annotations-for-object-detection-in-art","slug":"no-annotations-for-object-detection-in-art","title":"No Annotations for Object Detection in Art through Stable Diffusion","date":"2024-12-09","arxiv_id":"2412.06286","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-using-event-camera-a-moe","slug":"object-detection-using-event-camera-a-moe","title":"Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset","date":"2024-12-09","arxiv_id":"2412.06647","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-3d-object-detection-using","slug":"real-time-3d-object-detection-using","title":"Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator","date":"2024-12-07","arxiv_id":"2412.05594","repositories_listed":1,"syntology":null},{"url":"/paper/deyolo-dual-feature-enhancement-yolo-for","slug":"deyolo-dual-feature-enhancement-yolo-for","title":"DEYOLO: Dual-Feature-Enhancement YOLO for Cross-Modality Object Detection","date":"2024-12-06","arxiv_id":"2412.04931","repositories_listed":1,"syntology":null},{"url":"/paper/hola-hololens-object-labeling","slug":"hola-hololens-object-labeling","title":"HOLa: HoloLens Object Labeling","date":"2024-12-06","arxiv_id":"2412.04945","repositories_listed":1,"syntology":null},{"url":"/paper/towards-flexible-3d-perception-object-centric","slug":"towards-flexible-3d-perception-object-centric","title":"Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection","date":"2024-12-06","arxiv_id":"2412.05154","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":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-flexible-3d-perception-object-centric#ran","syntology_url":"https://syntology.ai/paper/2412.05154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05154"}},"official":{"repos":["ghostish/objectcentricocccompletion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cubify-anything-scaling-indoor-3d-object","slug":"cubify-anything-scaling-indoor-3d-object","title":"Cubify Anything: Scaling Indoor 3D Object Detection","date":"2024-12-05","arxiv_id":"2412.04458","repositories_listed":1,"syntology":null},{"url":"/paper/evrt-detr-the-surprising-effectiveness-of","slug":"evrt-detr-the-surprising-effectiveness-of","title":"EvRT-DETR: Latent Space Adaptation of Image Detectors for Event-based Vision","date":"2024-12-03","arxiv_id":"2412.02890","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evrt-detr-the-surprising-effectiveness-of#ran","syntology_url":"https://syntology.ai/paper/2412.02890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02890"}},"official":{"repos":["realtime-intelligence/evrt-detr"],"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/gaussian-object-carver-object-compositional","slug":"gaussian-object-carver-object-compositional","title":"Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion","date":"2024-12-03","arxiv_id":"2412.02075","repositories_listed":1,"syntology":null},{"url":"/paper/gsot3d-towards-generic-3d-single-object","slug":"gsot3d-towards-generic-3d-single-object","title":"GSOT3D: Towards Generic 3D Single Object Tracking in the Wild","date":"2024-12-03","arxiv_id":"2412.02129","repositories_listed":1,"syntology":null},{"url":"/paper/mftf-mask-free-training-free-object-level","slug":"mftf-mask-free-training-free-object-level","title":"MFTF: Mask-free Training-free Object Level Layout Control Diffusion Model","date":"2024-12-02","arxiv_id":"2412.01284","repositories_listed":1,"syntology":null},{"url":"/paper/multi-granularity-video-object-segmentation","slug":"multi-granularity-video-object-segmentation","title":"Multi-Granularity Video Object Segmentation","date":"2024-12-02","arxiv_id":"2412.01471","repositories_listed":1,"syntology":null},{"url":"/paper/referring-video-object-segmentation-via","slug":"referring-video-object-segmentation-via","title":"Referring Video Object Segmentation via Language-aligned Track Selection","date":"2024-12-02","arxiv_id":"2412.01136","repositories_listed":1,"syntology":null},{"url":"/paper/particle-based-6d-object-pose-estimation-from","slug":"particle-based-6d-object-pose-estimation-from","title":"Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models","date":"2024-12-01","arxiv_id":"2412.00835","repositories_listed":1,"syntology":null},{"url":"/paper/feedback-driven-object-detection-and","slug":"feedback-driven-object-detection-and","title":"Feedback-driven object detection and iterative model improvement","date":"2024-11-29","arxiv_id":"2411.19835","repositories_listed":1,"syntology":null},{"url":"/paper/great-geometry-intention-collaborative","slug":"great-geometry-intention-collaborative","title":"GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding","date":"2024-11-29","arxiv_id":"2411.19626","repositories_listed":1,"syntology":null},{"url":"/paper/detailed-object-description-with-controllable","slug":"detailed-object-description-with-controllable","title":"Detailed Object Description with Controllable Dimensions","date":"2024-11-28","arxiv_id":"2411.19106","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-track-anything","slug":"efficient-track-anything","title":"Efficient Track Anything","date":"2024-11-28","arxiv_id":"2411.18933","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":0,"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/efficient-track-anything#ran","syntology_url":"https://syntology.ai/paper/2411.18933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.18933"}},"official":null}},{"url":"/paper/lost-found-updating-dynamic-3d-scene-graphs","slug":"lost-found-updating-dynamic-3d-scene-graphs","title":"Lost & Found: Tracking Changes from Egocentric Observations in 3D Dynamic Scene Graphs","date":"2024-11-28","arxiv_id":"2411.19162","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lost-found-updating-dynamic-3d-scene-graphs#ran","syntology_url":"https://syntology.ai/paper/2411.19162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19162"}},"official":{"repos":["behretj/LostFound"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sadg-segment-any-dynamic-gaussian-without","slug":"sadg-segment-any-dynamic-gaussian-without","title":"SADG: Segment Any Dynamic Gaussian Without Object Trackers","date":"2024-11-28","arxiv_id":"2411.19290","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/sadg-segment-any-dynamic-gaussian-without#ran","syntology_url":"https://syntology.ai/paper/2411.19290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19290"}},"official":{"repos":["yunjinli/SADG-SegmentAnyDynamicGaussian"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/from-open-vocabulary-to-open-world-teaching","slug":"from-open-vocabulary-to-open-world-teaching","title":"From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects","date":"2024-11-27","arxiv_id":"2411.18207","repositories_listed":1,"syntology":null},{"url":"/paper/g3flow-generative-3d-semantic-flow-for-pose","slug":"g3flow-generative-3d-semantic-flow-for-pose","title":"G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation","date":"2024-11-27","arxiv_id":"2411.18369","repositories_listed":1,"syntology":null},{"url":"/paper/spotlight-shadow-guided-object-relighting-via","slug":"spotlight-shadow-guided-object-relighting-via","title":"SpotLight: Shadow-Guided Object Relighting via Diffusion","date":"2024-11-27","arxiv_id":"2411.18665","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-bounding-boxes-generation-abbg","slug":"adversarial-bounding-boxes-generation-abbg","title":"Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers","date":"2024-11-26","arxiv_id":"2411.17468","repositories_listed":1,"syntology":null},{"url":"/paper/box-for-mask-and-mask-for-box-weak-losses-for","slug":"box-for-mask-and-mask-for-box-weak-losses-for","title":"Box for Mask and Mask for Box: weak losses for multi-task partially supervised learning","date":"2024-11-26","arxiv_id":"2411.17536","repositories_listed":1,"syntology":null},{"url":"/paper/dreammix-decoupling-object-attributes-for","slug":"dreammix-decoupling-object-attributes-for","title":"DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting","date":"2024-11-26","arxiv_id":"2411.17223","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-proto-symbolic-behavioural","slug":"object-centric-proto-symbolic-behavioural","title":"Object-centric proto-symbolic behavioural reasoning from pixels","date":"2024-11-26","arxiv_id":"2411.17438","repositories_listed":1,"syntology":null},{"url":"/paper/cia-controllable-image-augmentation-framework","slug":"cia-controllable-image-augmentation-framework","title":"CIA: Controllable Image Augmentation Framework Based on Stable Diffusion","date":"2024-11-25","arxiv_id":"2411.16128","repositories_listed":1,"syntology":null},{"url":"/paper/intragen-trajectory-controlled-video","slug":"intragen-trajectory-controlled-video","title":"InTraGen: Trajectory-controlled Video Generation for Object Interactions","date":"2024-11-25","arxiv_id":"2411.16804","repositories_listed":1,"syntology":null},{"url":"/paper/open-vocabulary-monocular-3d-object-detection","slug":"open-vocabulary-monocular-3d-object-detection","title":"Open Vocabulary Monocular 3D Object Detection","date":"2024-11-25","arxiv_id":"2411.16833","repositories_listed":1,"syntology":null},{"url":"/paper/lrsaa-large-scale-remote-sensing-image-target","slug":"lrsaa-large-scale-remote-sensing-image-target","title":"LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic Annotation","date":"2024-11-24","arxiv_id":"2411.15808","repositories_listed":1,"syntology":null},{"url":"/paper/towards-raw-object-detection-in-diverse","slug":"towards-raw-object-detection-in-diverse","title":"Towards RAW Object Detection in Diverse Conditions","date":"2024-11-24","arxiv_id":"2411.15678","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":2,"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/towards-raw-object-detection-in-diverse#ran","syntology_url":"https://syntology.ai/paper/2411.15678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.15678"}},"official":{"repos":["lzyhha/aodraw"],"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/ocdet-object-center-detection-via-bounding","slug":"ocdet-object-center-detection-via-bounding","title":"OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs","date":"2024-11-23","arxiv_id":"2411.15653","repositories_listed":1,"syntology":null},{"url":"/paper/dino-x-a-unified-vision-model-for-open-world","slug":"dino-x-a-unified-vision-model-for-open-world","title":"DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding","date":"2024-11-21","arxiv_id":"2411.14347","repositories_listed":1,"syntology":null},{"url":"/paper/easyhoi-unleashing-the-power-of-large-models","slug":"easyhoi-unleashing-the-power-of-large-models","title":"EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild","date":"2024-11-21","arxiv_id":"2411.14280","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-thermal-mot-a-novel-box-association","slug":"enhancing-thermal-mot-a-novel-box-association","title":"Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity","date":"2024-11-20","arxiv_id":"2411.12943","repositories_listed":1,"syntology":null},{"url":"/paper/find-any-part-in-3d","slug":"find-any-part-in-3d","title":"Find Any Part in 3D","date":"2024-11-20","arxiv_id":"2411.13550","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/find-any-part-in-3d#ran","syntology_url":"https://syntology.ai/paper/2411.13550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13550"}},"official":null}},{"url":"/paper/teaching-vlms-to-localize-specific-objects","slug":"teaching-vlms-to-localize-specific-objects","title":"Teaching VLMs to Localize Specific Objects from In-context Examples","date":"2024-11-20","arxiv_id":"2411.13317","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/teaching-vlms-to-localize-specific-objects#ran","syntology_url":"https://syntology.ai/paper/2411.13317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13317"}},"official":{"repos":["sivandoveh/iploc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"8c2306853f90c75c6c38cd9de51002cafa150be1504c9a50cda9e0d5dba609f3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}