{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-detection-1/papers/32","list_of":"/task/object-detection-1","task":"object-detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":32,"pages_in_order":106,"rows_per_page":100,"rows":[3101,3200],"of":10514,"counts":{"archive_papers_tagged":10514,"with_a_code_link":4285,"where_syntology_ran_a_sample":1027,"not_listed_spam_title":0,"listed":10514,"listed_where_code_ran":1027,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":898,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":898,"listed_every_run_a_failure_of_syntologys_instrument":129,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-detection-1","prev":"/task/object-detection-1/papers/31","next":"/task/object-detection-1/papers/33","papers":[{"url":"/paper/peco-perceptual-codebook-for-bert-pre","slug":"peco-perceptual-codebook-for-bert-pre","title":"PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers","date":"2021-11-24","arxiv_id":"2111.12710","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-object-detection-via-association-and-1","slug":"few-shot-object-detection-via-association-and-1","title":"Few-Shot Object Detection via Association and DIscrimination","date":"2021-11-23","arxiv_id":"2111.11656","repositories_listed":1,"syntology":null},{"url":"/paper/focal-and-global-knowledge-distillation-for","slug":"focal-and-global-knowledge-distillation-for","title":"Focal and Global Knowledge Distillation for Detectors","date":"2021-11-23","arxiv_id":"2111.11837","repositories_listed":1,"syntology":null},{"url":"/paper/fedcv-a-federated-learning-framework-for","slug":"fedcv-a-federated-learning-framework-for","title":"FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks","date":"2021-11-22","arxiv_id":"2111.11066","repositories_listed":1,"syntology":null},{"url":"/paper/l-verse-bidirectional-generation-between","slug":"l-verse-bidirectional-generation-between","title":"L-Verse: Bidirectional Generation Between Image and Text","date":"2021-11-22","arxiv_id":"2111.11133","repositories_listed":1,"syntology":null},{"url":"/paper/many-heads-but-one-brain-an-overview-of","slug":"many-heads-but-one-brain-an-overview-of","title":"Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture","date":"2021-11-22","arxiv_id":"2111.10974","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-transformers-excel-at-class","slug":"multi-modal-transformers-excel-at-class","title":"Class-agnostic Object Detection with Multi-modal Transformer","date":"2021-11-22","arxiv_id":"2111.11430","repositories_listed":1,"syntology":null},{"url":"/paper/mum-mix-image-tiles-and-unmix-feature-tiles","slug":"mum-mix-image-tiles-and-unmix-feature-tiles","title":"MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection","date":"2021-11-22","arxiv_id":"2111.10958","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-grow-finish-pigs-across-large-pens","slug":"tracking-grow-finish-pigs-across-large-pens","title":"Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras","date":"2021-11-22","arxiv_id":"2111.10971","repositories_listed":1,"syntology":null},{"url":"/paper/raanet-range-aware-attention-network-for","slug":"raanet-range-aware-attention-network-for","title":"Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation","date":"2021-11-18","arxiv_id":"2111.09515","repositories_listed":1,"syntology":null},{"url":"/paper/towards-open-vocabulary-object-detection","slug":"towards-open-vocabulary-object-detection","title":"Open Vocabulary Object Detection with Pseudo Bounding-Box Labels","date":"2021-11-18","arxiv_id":"2111.09452","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"6 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; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/towards-open-vocabulary-object-detection#ran","syntology_url":"https://syntology.ai/paper/2111.09452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09452"}},"official":{"repos":["salesforce/pb-ovd"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/arkitscenes-a-diverse-real-world-dataset-for","slug":"arkitscenes-a-diverse-real-world-dataset-for","title":"ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data","date":"2021-11-17","arxiv_id":"2111.08897","repositories_listed":1,"syntology":null},{"url":"/paper/sapnet-segmentation-aware-progressive-network","slug":"sapnet-segmentation-aware-progressive-network","title":"SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining","date":"2021-11-17","arxiv_id":"2111.08892","repositories_listed":1,"syntology":null},{"url":"/paper/tyolov5-a-temporal-yolov5-detector-based-on","slug":"tyolov5-a-temporal-yolov5-detector-based-on","title":"TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video","date":"2021-11-17","arxiv_id":"2111.08867","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-vision-language-pre-training","slug":"multi-grained-vision-language-pre-training","title":"Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts","date":"2021-11-16","arxiv_id":"2111.08276","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-grained-vision-language-pre-training#ran","syntology_url":"https://syntology.ai/paper/2111.08276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08276"}},"official":{"repos":["zengyan-97/x-vlm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/single-stage-uav-detection-and-classification","slug":"single-stage-uav-detection-and-classification","title":"Single-stage uav detection and classification with yolov5: Mosaic data augmentation and panet","date":"2021-11-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/attention-mechanisms-in-computer-vision-a","slug":"attention-mechanisms-in-computer-vision-a","title":"Attention Mechanisms in Computer Vision: A Survey","date":"2021-11-15","arxiv_id":"2111.07624","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-grounded-object-matching-for","slug":"semantically-grounded-object-matching-for","title":"Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement","date":"2021-11-15","arxiv_id":"2111.07975","repositories_listed":1,"syntology":null},{"url":"/paper/co-segmentation-inspired-attention-module-for","slug":"co-segmentation-inspired-attention-module-for","title":"Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks","date":"2021-11-14","arxiv_id":"2111.07370","repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-accurate-object-detection-via-self","slug":"robust-and-accurate-object-detection-via-self","title":"Robust and Accurate Object Detection via Self-Knowledge Distillation","date":"2021-11-14","arxiv_id":"2111.07239","repositories_listed":1,"syntology":null},{"url":"/paper/attention-guided-cosine-margin-for-overcoming","slug":"attention-guided-cosine-margin-for-overcoming","title":"Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection","date":"2021-11-12","arxiv_id":"2111.06639","repositories_listed":1,"syntology":null},{"url":"/paper/indian-licence-plate-dataset-in-the-wild","slug":"indian-licence-plate-dataset-in-the-wild","title":"Indian Licence Plate Dataset in the wild","date":"2021-11-11","arxiv_id":"2111.06054","repositories_listed":1,"syntology":null},{"url":"/paper/fast-camouflaged-object-detection-via-edge","slug":"fast-camouflaged-object-detection-via-edge","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","date":"2021-11-05","arxiv_id":"2111.03216","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-camouflaged-object-detection-via-edge#ran","syntology_url":"https://syntology.ai/paper/2111.03216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.03216"}},"official":{"repos":["gewelsji/errnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/addressing-multiple-salient-object-detection","slug":"addressing-multiple-salient-object-detection","title":"Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies","date":"2021-11-04","arxiv_id":"2111.03195","repositories_listed":1,"syntology":null},{"url":"/paper/bootstrap-your-object-detector-via-mixed","slug":"bootstrap-your-object-detector-via-mixed","title":"Bootstrap Your Object Detector via Mixed Training","date":"2021-11-04","arxiv_id":"2111.03056","repositories_listed":1,"syntology":null},{"url":"/paper/absolute-distance-prediction-based-on-deep","slug":"absolute-distance-prediction-based-on-deep","title":"Absolute distance prediction based on deep learning object detection and monocular depth estimation models","date":"2021-11-02","arxiv_id":"2111.01715","repositories_listed":1,"syntology":null},{"url":"/paper/adapool-exponential-adaptive-pooling-for","slug":"adapool-exponential-adaptive-pooling-for","title":"AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling","date":"2021-11-01","arxiv_id":"2111.00772","repositories_listed":1,"syntology":null},{"url":"/paper/sign-to-speech-model-for-sign-language","slug":"sign-to-speech-model-for-sign-language","title":"Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language","date":"2021-11-01","arxiv_id":"2111.00995","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modality-fusion-transformer-for","slug":"cross-modality-fusion-transformer-for","title":"Cross-Modality Fusion Transformer for Multispectral Object Detection","date":"2021-10-30","arxiv_id":"2111.00273","repositories_listed":1,"syntology":null},{"url":"/paper/improving-camouflaged-object-detection-with","slug":"improving-camouflaged-object-detection-with","title":"Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels","date":"2021-10-29","arxiv_id":"2110.15606","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-non-co-occurrence-with-unlabeled-in","slug":"bridging-non-co-occurrence-with-unlabeled-in","title":"Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection","date":"2021-10-28","arxiv_id":"2110.15017","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/bridging-non-co-occurrence-with-unlabeled-in#ran","syntology_url":"https://syntology.ai/paper/2110.15017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15017"}},"official":{"repos":["dongnana777/bridging-non-co-occurrence"],"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/facial-emotion-recognition-a-multi-task","slug":"facial-emotion-recognition-a-multi-task","title":"Facial Emotion Recognition: A multi-task approach using deep learning","date":"2021-10-28","arxiv_id":"2110.15028","repositories_listed":1,"syntology":null},{"url":"/paper/mcunetv2-memory-efficient-patch-based","slug":"mcunetv2-memory-efficient-patch-based","title":"MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning","date":"2021-10-28","arxiv_id":"2110.15352","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-self-supervised-and-few-shot","slug":"a-survey-of-self-supervised-and-few-shot","title":"A Survey of Self-Supervised and Few-Shot Object Detection","date":"2021-10-27","arxiv_id":"2110.14711","repositories_listed":1,"syntology":null},{"url":"/paper/mixed-supervised-object-detection-by","slug":"mixed-supervised-object-detection-by","title":"Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity","date":"2021-10-27","arxiv_id":"2110.14191","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/mixed-supervised-object-detection-by#ran","syntology_url":"https://syntology.ai/paper/2110.14191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14191"}},"official":{"repos":["bcmi/tramas-weak-shot-object-detection"],"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/alpha-iou-a-family-of-power-intersection-over","slug":"alpha-iou-a-family-of-power-intersection-over","title":"Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression","date":"2021-10-26","arxiv_id":"2110.13675","repositories_listed":1,"syntology":null},{"url":"/paper/plug-and-play-few-shot-object-detection-with","slug":"plug-and-play-few-shot-object-detection-with","title":"Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference","date":"2021-10-26","arxiv_id":"2110.13377","repositories_listed":1,"syntology":null},{"url":"/paper/yolo-ret-towards-high-accuracy-real-time","slug":"yolo-ret-towards-high-accuracy-real-time","title":"YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs","date":"2021-10-26","arxiv_id":"2110.13713","repositories_listed":1,"syntology":null},{"url":"/paper/diagnosing-errors-in-video-relation-detectors","slug":"diagnosing-errors-in-video-relation-detectors","title":"Diagnosing Errors in Video Relation Detectors","date":"2021-10-25","arxiv_id":"2110.13110","repositories_listed":1,"syntology":null},{"url":"/paper/instance-conditional-knowledge-distillation","slug":"instance-conditional-knowledge-distillation","title":"Instance-Conditional Knowledge Distillation for Object Detection","date":"2021-10-25","arxiv_id":"2110.12724","repositories_listed":1,"syntology":null},{"url":"/paper/cova-context-aware-visual-attention-for","slug":"cova-context-aware-visual-attention-for","title":"CoVA: Context-aware Visual Attention for Webpage Information Extraction","date":"2021-10-24","arxiv_id":"2110.12320","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/cova-context-aware-visual-attention-for#ran","syntology_url":"https://syntology.ai/paper/2110.12320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.12320"}},"official":{"repos":["kevalmorabia97/cova-web-object-detection"],"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/cvt-assd-convolutional-vision-transformer","slug":"cvt-assd-convolutional-vision-transformer","title":"CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector","date":"2021-10-24","arxiv_id":"2110.12364","repositories_listed":1,"syntology":null},{"url":"/paper/nas-fcos-efficient-search-for-object","slug":"nas-fcos-efficient-search-for-object","title":"NAS-FCOS: Efficient Search for Object Detection Architectures","date":"2021-10-24","arxiv_id":"2110.12423","repositories_listed":1,"syntology":null},{"url":"/paper/ceymo-see-more-on-roads-a-novel-benchmark","slug":"ceymo-see-more-on-roads-a-novel-benchmark","title":"CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection","date":"2021-10-22","arxiv_id":"2110.11867","repositories_listed":1,"syntology":null},{"url":"/paper/circle-representation-for-medical-object","slug":"circle-representation-for-medical-object","title":"Circle Representation for Medical Object Detection","date":"2021-10-22","arxiv_id":"2110.12093","repositories_listed":1,"syntology":null},{"url":"/paper/recurrence-along-depth-deep-convolutional","slug":"recurrence-along-depth-deep-convolutional","title":"Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation","date":"2021-10-22","arxiv_id":"2110.11852","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/recurrence-along-depth-deep-convolutional#ran","syntology_url":"https://syntology.ai/paper/2110.11852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11852"}},"official":{"repos":["fangyanwen1106/RLANet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sequential-decision-making-for-active-object","slug":"sequential-decision-making-for-active-object","title":"Sequential Voting with Relational Box Fields for Active Object Detection","date":"2021-10-21","arxiv_id":"2110.11524","repositories_listed":1,"syntology":null},{"url":"/paper/does-data-repair-lead-to-fair-models-curating","slug":"does-data-repair-lead-to-fair-models-curating","title":"Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias","date":"2021-10-20","arxiv_id":"2110.10389","repositories_listed":1,"syntology":null},{"url":"/paper/improving-model-generalization-by-agreement","slug":"improving-model-generalization-by-agreement","title":"Improving Model Generalization by Agreement of Learned Representations from Data Augmentation","date":"2021-10-20","arxiv_id":"2110.10536","repositories_listed":1,"syntology":null},{"url":"/paper/model-composition-can-multiple-neural","slug":"model-composition-can-multiple-neural","title":"Model Composition: Can Multiple Neural Networks Be Combined into a Single Network Using Only Unlabeled Data?","date":"2021-10-20","arxiv_id":"2110.10369","repositories_listed":1,"syntology":null},{"url":"/paper/nod-taking-a-closer-look-at-detection-under","slug":"nod-taking-a-closer-look-at-detection-under","title":"NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset","date":"2021-10-20","arxiv_id":"2110.10364","repositories_listed":1,"syntology":null},{"url":"/paper/repaint-improving-the-generalization-of-down","slug":"repaint-improving-the-generalization-of-down","title":"Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples","date":"2021-10-20","arxiv_id":"2110.10366","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-distillation-aggregating-knowledge","slug":"adaptive-distillation-aggregating-knowledge","title":"Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation","date":"2021-10-19","arxiv_id":"2110.09674","repositories_listed":1,"syntology":null},{"url":"/paper/hm-net-a-regression-network-for-object-center","slug":"hm-net-a-regression-network-for-object-center","title":"HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery","date":"2021-10-19","arxiv_id":"2110.09881","repositories_listed":1,"syntology":null},{"url":"/paper/video-data-pipelines-for-machine-learning","slug":"video-data-pipelines-for-machine-learning","title":"Video-Data Pipelines for Machine Learning Applications","date":"2021-10-15","arxiv_id":"2110.11407","repositories_listed":1,"syntology":null},{"url":"/paper/detr3d-3d-object-detection-from-multi-view","slug":"detr3d-3d-object-detection-from-multi-view","title":"DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries","date":"2021-10-13","arxiv_id":"2110.06922","repositories_listed":1,"syntology":null},{"url":"/paper/object-dgcnn-3d-object-detection-using","slug":"object-dgcnn-3d-object-detection-using","title":"Object DGCNN: 3D Object Detection using Dynamic Graphs","date":"2021-10-13","arxiv_id":"2110.06923","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":3,"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/object-dgcnn-3d-object-detection-using#ran","syntology_url":"https://syntology.ai/paper/2110.06923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06923"}},"official":{"repos":["wangyueft/detr3d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revitalizing-cnn-attentions-via-transformers","slug":"revitalizing-cnn-attentions-via-transformers","title":"Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning","date":"2021-10-11","arxiv_id":"2110.05340","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":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/revitalizing-cnn-attentions-via-transformers#ran","syntology_url":"https://syntology.ai/paper/2110.05340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.05340"}},"official":{"repos":["chongjiange/care"],"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/morphable-detector-for-object-detection-on-1","slug":"morphable-detector-for-object-detection-on-1","title":"Morphable Detector for Object Detection on Demand","date":"2021-10-10","arxiv_id":"2110.04917","repositories_listed":1,"syntology":null},{"url":"/paper/trident-pyramid-networks-the-importance-of","slug":"trident-pyramid-networks-the-importance-of","title":"Trident Pyramid Networks: The importance of processing at the feature pyramid level for better object detection","date":"2021-10-08","arxiv_id":"2110.04004","repositories_listed":1,"syntology":null},{"url":"/paper/vidt-an-efficient-and-effective-fully","slug":"vidt-an-efficient-and-effective-fully","title":"ViDT: An Efficient and Effective Fully Transformer-based Object Detector","date":"2021-10-08","arxiv_id":"2110.03921","repositories_listed":1,"syntology":null},{"url":"/paper/mgpsn-motion-guided-pseudo-siamese-network","slug":"mgpsn-motion-guided-pseudo-siamese-network","title":"MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings","date":"2021-10-07","arxiv_id":"2110.03302","repositories_listed":1,"syntology":null},{"url":"/paper/fod-a-a-dataset-for-foreign-object-debris-in","slug":"fod-a-a-dataset-for-foreign-object-debris-in","title":"FOD-A: A Dataset for Foreign Object Debris in Airports","date":"2021-10-06","arxiv_id":"2110.03072","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-metacognition-for-object-detection","slug":"learning-a-metacognition-for-object-detection","title":"MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations","date":"2021-10-06","arxiv_id":"2110.03105","repositories_listed":1,"syntology":null},{"url":"/paper/long-tailed-distribution-adaptation","slug":"long-tailed-distribution-adaptation","title":"Long-tailed Distribution Adaptation","date":"2021-10-06","arxiv_id":"2110.02686","repositories_listed":1,"syntology":null},{"url":"/paper/s-extension-patch-a-simple-and-efficient-way","slug":"s-extension-patch-a-simple-and-efficient-way","title":"S-Extension Patch: A simple and efficient way to extend an object detection model","date":"2021-10-06","arxiv_id":"2110.02670","repositories_listed":1,"syntology":null},{"url":"/paper/weak-novel-categories-without-tears-a-survey","slug":"weak-novel-categories-without-tears-a-survey","title":"Weak Novel Categories without Tears: A Survey on Weak-Shot Learning","date":"2021-10-06","arxiv_id":"2110.02651","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-free-oriented-proposal-generator-for","slug":"anchor-free-oriented-proposal-generator-for","title":"Anchor-free Oriented Proposal Generator for Object Detection","date":"2021-10-05","arxiv_id":"2110.01931","repositories_listed":1,"syntology":null},{"url":"/paper/sound-event-detection-transformer-an-event","slug":"sound-event-detection-transformer-an-event","title":"Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection","date":"2021-10-05","arxiv_id":"2110.02011","repositories_listed":1,"syntology":null},{"url":"/paper/top-n-equivariant-set-and-graph-generation","slug":"top-n-equivariant-set-and-graph-generation","title":"Top-N: Equivariant set and graph generation without exchangeability","date":"2021-10-05","arxiv_id":"2110.02096","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/top-n-equivariant-set-and-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2110.02096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02096"}},"official":{"repos":["cvignac/top-n"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/transformer-assisted-convolutional-network","slug":"transformer-assisted-convolutional-network","title":"Transformer Assisted Convolutional Network for Cell Instance Segmentation","date":"2021-10-05","arxiv_id":"2110.02270","repositories_listed":1,"syntology":null},{"url":"/paper/dardet-a-dense-anchor-free-rotated-object","slug":"dardet-a-dense-anchor-free-rotated-object","title":"DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images","date":"2021-10-03","arxiv_id":"2110.01025","repositories_listed":1,"syntology":null},{"url":"/paper/moving-object-detection-for-event-based-2","slug":"moving-object-detection-for-event-based-2","title":"Moving Object Detection for Event-based vision using Graph Spectral Clustering","date":"2021-09-30","arxiv_id":"2109.14979","repositories_listed":1,"syntology":null},{"url":"/paper/towards-rotation-invariance-in-object","slug":"towards-rotation-invariance-in-object","title":"Towards Rotation Invariance in Object Detection","date":"2021-09-28","arxiv_id":"2109.13488","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-gaussian-heatmap-labeling-for","slug":"a-general-gaussian-heatmap-labeling-for","title":"A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection","date":"2021-09-27","arxiv_id":"2109.12848","repositories_listed":1,"syntology":null},{"url":"/paper/deep-structured-instance-graph-for-distilling","slug":"deep-structured-instance-graph-for-distilling","title":"Deep Structured Instance Graph for Distilling Object Detectors","date":"2021-09-27","arxiv_id":"2109.12862","repositories_listed":1,"syntology":null},{"url":"/paper/experience-feedback-using-representation","slug":"experience-feedback-using-representation","title":"Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images","date":"2021-09-27","arxiv_id":"2109.13027","repositories_listed":1,"syntology":null},{"url":"/paper/qbox-partial-transfer-learning-with-active","slug":"qbox-partial-transfer-learning-with-active","title":"QBox: Partial Transfer Learning with Active Querying for Object Detection","date":"2021-09-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-principled-approach-to-failure-analysis-and","slug":"a-principled-approach-to-failure-analysis-and","title":"A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging","date":"2021-09-25","arxiv_id":"2109.12347","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"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, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-principled-approach-to-failure-analysis-and#ran","syntology_url":"https://syntology.ai/paper/2109.12347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.12347"}},"official":{"repos":["Rokken-lab6/Failure-Analysis-and-Model-Repairment"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-real-time-and-high-precision-method-for","slug":"a-real-time-and-high-precision-method-for","title":"A real-time and high-precision method for small traffic-signs recognition","date":"2021-09-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rsdet-point-based-modulated-loss-for-more","slug":"rsdet-point-based-modulated-loss-for-more","title":"RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection","date":"2021-09-24","arxiv_id":"2109.11906","repositories_listed":1,"syntology":null},{"url":"/paper/zsd-yolo-zero-shot-yolo-detection-using","slug":"zsd-yolo-zero-shot-yolo-detection-using","title":"Zero-shot Object Detection Through Vision-Language Embedding Alignment","date":"2021-09-24","arxiv_id":"2109.12066","repositories_listed":1,"syntology":null},{"url":"/paper/lgd-label-guided-self-distillation-for-object","slug":"lgd-label-guided-self-distillation-for-object","title":"LGD: Label-guided Self-distillation for Object Detection","date":"2021-09-23","arxiv_id":"2109.11496","repositories_listed":1,"syntology":null},{"url":"/paper/mvm3det-a-novel-method-for-multi-view","slug":"mvm3det-a-novel-method-for-multi-view","title":"MVM3Det: A Novel Method for Multi-view Monocular 3D Detection","date":"2021-09-22","arxiv_id":"2109.10473","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-confidence-calibration-for-epistemic","slug":"bayesian-confidence-calibration-for-epistemic","title":"Bayesian Confidence Calibration for Epistemic Uncertainty Modelling","date":"2021-09-21","arxiv_id":"2109.10092","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-real-time-facial-analysis-system","slug":"towards-a-real-time-facial-analysis-system","title":"Towards a Real-Time Facial Analysis System","date":"2021-09-21","arxiv_id":"2109.10393","repositories_listed":1,"syntology":null},{"url":"/paper/hptq-hardware-friendly-post-training","slug":"hptq-hardware-friendly-post-training","title":"HPTQ: Hardware-Friendly Post Training Quantization","date":"2021-09-19","arxiv_id":"2109.09113","repositories_listed":1,"syntology":null},{"url":"/paper/joint-distribution-alignment-via-adversarial","slug":"joint-distribution-alignment-via-adversarial","title":"Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection","date":"2021-09-19","arxiv_id":"2109.09033","repositories_listed":1,"syntology":null},{"url":"/paper/ai-accelerator-survey-and-trends","slug":"ai-accelerator-survey-and-trends","title":"AI Accelerator Survey and Trends","date":"2021-09-18","arxiv_id":"2109.08957","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-hybrid-transformer-learning-global","slug":"efficient-hybrid-transformer-learning-global","title":"UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery","date":"2021-09-18","arxiv_id":"2109.08937","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-hybrid-transformer-learning-global#ran","syntology_url":"https://syntology.ai/paper/2109.08937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.08937"}},"official":{"repos":["WangLibo1995/GeoSeg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-end-to-end-transformer-model-for-3d-object","slug":"an-end-to-end-transformer-model-for-3d-object","title":"An End-to-End Transformer Model for 3D Object Detection","date":"2021-09-16","arxiv_id":"2109.08141","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":3,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-end-to-end-transformer-model-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/2109.08141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.08141"}},"official":null}},{"url":"/paper/fca-learning-a-3d-full-coverage-vehicle","slug":"fca-learning-a-3d-full-coverage-vehicle","title":"FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack","date":"2021-09-15","arxiv_id":"2109.07193","repositories_listed":1,"syntology":null},{"url":"/paper/ffavod-feature-fusion-architecture-for-video","slug":"ffavod-feature-fusion-architecture-for-video","title":"FFAVOD: Feature Fusion Architecture for Video Object Detection","date":"2021-09-15","arxiv_id":"2109.07298","repositories_listed":1,"syntology":null},{"url":"/paper/pnp-detr-towards-efficient-visual-analysis","slug":"pnp-detr-towards-efficient-visual-analysis","title":"PnP-DETR: Towards Efficient Visual Analysis with Transformers","date":"2021-09-15","arxiv_id":"2109.07036","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/pnp-detr-towards-efficient-visual-analysis#ran","syntology_url":"https://syntology.ai/paper/2109.07036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07036"}},"official":{"repos":["twangnh/pnp-detr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/progressive-hard-case-mining-across-pyramid","slug":"progressive-hard-case-mining-across-pyramid","title":"Progressive Hard-case Mining across Pyramid Levels for Object Detection","date":"2021-09-15","arxiv_id":"2109.07217","repositories_listed":1,"syntology":null},{"url":"/paper/dafne-a-one-stage-anchor-free-deep-model-for","slug":"dafne-a-one-stage-anchor-free-deep-model-for","title":"DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection","date":"2021-09-13","arxiv_id":"2109.06148","repositories_listed":1,"syntology":null},{"url":"/paper/single-stage-keypoint-based-category-level","slug":"single-stage-keypoint-based-category-level","title":"Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image","date":"2021-09-13","arxiv_id":"2109.06161","repositories_listed":1,"syntology":null},{"url":"/paper/pq-transformer-jointly-parsing-3d-objects-and","slug":"pq-transformer-jointly-parsing-3d-objects-and","title":"PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds","date":"2021-09-12","arxiv_id":"2109.05566","repositories_listed":1,"syntology":null},{"url":"/paper/reconfigisp-reconfigurable-camera-image","slug":"reconfigisp-reconfigurable-camera-image","title":"ReconfigISP: Reconfigurable Camera Image Processing Pipeline","date":"2021-09-10","arxiv_id":"2109.04760","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":4,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 4 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) · 3 unverified","sample_list":"/paper/reconfigisp-reconfigurable-camera-image#ran","syntology_url":"https://syntology.ai/paper/2109.04760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04760"}},"official":null}},{"url":"/paper/temporal-roi-align-for-video-object","slug":"temporal-roi-align-for-video-object","title":"Temporal RoI Align for Video Object Recognition","date":"2021-09-08","arxiv_id":"2109.03495","repositories_listed":1,"syntology":null}],"record_sha256":"e21808c79c7f70e1051c09da18fbe5f63d7facee6d9d86f5eb8f9ad66f4764a5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}