{"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/activity-recognition/papers/2","list_of":"/task/activity-recognition","task":"Activity Recognition","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":2,"pages_in_order":14,"rows_per_page":100,"rows":[101,200],"of":1322,"counts":{"archive_papers_tagged":1322,"with_a_code_link":330,"where_syntology_ran_a_sample":40,"not_listed_spam_title":0,"listed":1322,"listed_where_code_ran":40,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":32,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":32,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/activity-recognition","prev":"/task/activity-recognition","next":"/task/activity-recognition/papers/3","papers":[{"url":"/paper/skeleton-based-group-activity-recognition-via","slug":"skeleton-based-group-activity-recognition-via","title":"Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph","date":"2024-07-28","arxiv_id":"2407.19497","repositories_listed":1,"syntology":null},{"url":"/paper/guidelines-for-augmentation-selection-in","slug":"guidelines-for-augmentation-selection-in","title":"Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification","date":"2024-07-12","arxiv_id":"2407.09336","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/guidelines-for-augmentation-selection-in#ran","syntology_url":"https://syntology.ai/paper/2407.09336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09336"}},"official":{"repos":["dl4mhealth/ts-contrastive-augmentation-recommendation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/accurate-passive-radar-via-an-uncertainty","slug":"accurate-passive-radar-via-an-uncertainty","title":"Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data","date":"2024-07-01","arxiv_id":"2407.04733","repositories_listed":1,"syntology":null},{"url":"/paper/neuro-symbolic-fusion-of-wi-fi-sensing-data","slug":"neuro-symbolic-fusion-of-wi-fi-sensing-data","title":"Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer","date":"2024-07-01","arxiv_id":"2407.04734","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-lda-feature-extraction-to-augment","slug":"leveraging-lda-feature-extraction-to-augment","title":"Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy","date":"2024-06-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/actnetformer-transformer-resnet-hybrid-method","slug":"actnetformer-transformer-resnet-hybrid-method","title":"ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos","date":"2024-04-09","arxiv_id":"2404.06243","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-self-supervised-learning-techniques","slug":"comparing-self-supervised-learning-techniques","title":"Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition","date":"2024-04-08","arxiv_id":"2404.15331","repositories_listed":1,"syntology":null},{"url":"/paper/learning-alternative-ways-of-performing-a","slug":"learning-alternative-ways-of-performing-a","title":"Learning Alternative Ways of Performing a Task","date":"2024-04-03","arxiv_id":"2404.02579","repositories_listed":1,"syntology":null},{"url":"/paper/harmamba-efficient-wearable-sensor-human","slug":"harmamba-efficient-wearable-sensor-human","title":"HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba","date":"2024-03-29","arxiv_id":"2403.20183","repositories_listed":1,"syntology":null},{"url":"/paper/activity-biometrics-person-identification","slug":"activity-biometrics-person-identification","title":"Activity-Biometrics: Person Identification from Daily Activities","date":"2024-03-26","arxiv_id":"2403.17360","repositories_listed":1,"syntology":null},{"url":"/paper/neuflow-real-time-high-accuracy-optical-flow","slug":"neuflow-real-time-high-accuracy-optical-flow","title":"NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices","date":"2024-03-15","arxiv_id":"2403.10425","repositories_listed":1,"syntology":null},{"url":"/paper/ditmos-delving-into-diverse-tiny-model","slug":"ditmos-delving-into-diverse-tiny-model","title":"DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers","date":"2024-03-14","arxiv_id":"2403.09035","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-relevance-learning-grassmann","slug":"generalized-relevance-learning-grassmann","title":"Generalized Relevance Learning Grassmann Quantization","date":"2024-03-14","arxiv_id":"2403.09183","repositories_listed":1,"syntology":null},{"url":"/paper/class-incremental-learning-for-time-series","slug":"class-incremental-learning-for-time-series","title":"Class-incremental Learning for Time Series: Benchmark and Evaluation","date":"2024-02-19","arxiv_id":"2402.12035","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/class-incremental-learning-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2402.12035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12035"}},"official":{"repos":["zqiao11/tscil"],"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/the-visual-experience-dataset-over-200","slug":"the-visual-experience-dataset-over-200","title":"The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video","date":"2024-02-15","arxiv_id":"2404.18934","repositories_listed":1,"syntology":null},{"url":"/paper/ossar-towards-open-set-surgical-activity","slug":"ossar-towards-open-set-surgical-activity","title":"OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery","date":"2024-02-10","arxiv_id":"2402.06985","repositories_listed":1,"syntology":null},{"url":"/paper/autogcn-towards-generic-human-activity","slug":"autogcn-towards-generic-human-activity","title":"AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search","date":"2024-02-02","arxiv_id":"2402.01313","repositories_listed":1,"syntology":null},{"url":"/paper/imugpt-2-0-language-based-cross-modality","slug":"imugpt-2-0-language-based-cross-modality","title":"IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition","date":"2024-02-01","arxiv_id":"2402.01049","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/imugpt-2-0-language-based-cross-modality#ran","syntology_url":"https://syntology.ai/paper/2402.01049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01049"}},"official":null}},{"url":"/paper/mifi-multi-camera-feature-integration-for","slug":"mifi-multi-camera-feature-integration-for","title":"MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition","date":"2024-01-25","arxiv_id":"2401.14115","repositories_listed":1,"syntology":null},{"url":"/paper/wimans-a-benchmark-dataset-for-wifi-based","slug":"wimans-a-benchmark-dataset-for-wifi-based","title":"WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing","date":"2024-01-24","arxiv_id":"2402.09430","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wimans-a-benchmark-dataset-for-wifi-based#ran","syntology_url":"https://syntology.ai/paper/2402.09430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09430"}},"official":{"repos":["huangshk/wimans"],"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/a-review-of-deep-learning-methods-for-1","slug":"a-review-of-deep-learning-methods-for-1","title":"A Review of Deep Learning Methods for Photoplethysmography Data","date":"2024-01-23","arxiv_id":"2401.12783","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-bridge-based-mobile-former","slug":"uncertainty-aware-bridge-based-mobile-former","title":"Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition","date":"2024-01-20","arxiv_id":"2401.11123","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-techniques-for-cross-domain","slug":"data-augmentation-techniques-for-cross-domain","title":"Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition","date":"2024-01-01","arxiv_id":"2401.00964","repositories_listed":1,"syntology":null},{"url":"/paper/directional-antenna-systems-for-long-range","slug":"directional-antenna-systems-for-long-range","title":"Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition","date":"2024-01-01","arxiv_id":"2401.01388","repositories_listed":1,"syntology":null},{"url":"/paper/challenges-in-multi-centric-generalization","slug":"challenges-in-multi-centric-generalization","title":"Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery","date":"2023-12-18","arxiv_id":"2312.11250","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"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) · 0 unverified","sample_list":"/paper/challenges-in-multi-centric-generalization#ran","syntology_url":"https://syntology.ai/paper/2312.11250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11250"}},"official":{"repos":["camma-public/multibypass140"],"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/deep-unsupervised-domain-adaptation-for-time","slug":"deep-unsupervised-domain-adaptation-for-time","title":"Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark","date":"2023-12-15","arxiv_id":"2312.09857","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-spatio-temporal-image-encoding-for","slug":"enhanced-spatio-temporal-image-encoding-for","title":"Enhanced Spatio- Temporal Image Encoding for Online Human Activity Recognition","date":"2023-12-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-stage-learning-for-radar-pulse-activity","slug":"multi-stage-learning-for-radar-pulse-activity","title":"Multi-stage Learning for Radar Pulse Activity Segmentation","date":"2023-12-15","arxiv_id":"2312.09489","repositories_listed":1,"syntology":null},{"url":"/paper/online-semi-supervised-learning-of-composite","slug":"online-semi-supervised-learning-of-composite","title":"Online Semi-Supervised Learning of Composite Event Rules by Combining Structure and Mass-Based Predicate Similarity","date":"2023-12-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-geometric-understanding-of-spatio","slug":"towards-a-geometric-understanding-of-spatio","title":"Towards a geometric understanding of Spatio Temporal Graph Convolution Networks","date":"2023-12-12","arxiv_id":"2312.07777","repositories_listed":1,"syntology":null},{"url":"/paper/navigating-open-set-scenarios-for-skeleton","slug":"navigating-open-set-scenarios-for-skeleton","title":"Navigating Open Set Scenarios for Skeleton-based Action Recognition","date":"2023-12-11","arxiv_id":"2312.06330","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"8 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/navigating-open-set-scenarios-for-skeleton#ran","syntology_url":"https://syntology.ai/paper/2312.06330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06330"}},"official":{"repos":["kpeng9510/os-sar"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/action-slot-visual-action-centric","slug":"action-slot-visual-action-centric","title":"Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic Scenes","date":"2023-11-29","arxiv_id":"2311.17948","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-action-localization-for-inertial","slug":"temporal-action-localization-for-inertial","title":"Temporal Action Localization for Inertial-based Human Activity Recognition","date":"2023-11-27","arxiv_id":"2311.15831","repositories_listed":1,"syntology":null},{"url":"/paper/quantized-distillation-optimizing-driver","slug":"quantized-distillation-optimizing-driver","title":"Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments","date":"2023-11-10","arxiv_id":"2311.05970","repositories_listed":1,"syntology":null},{"url":"/paper/optimization-free-test-time-adaptation-for","slug":"optimization-free-test-time-adaptation-for","title":"Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition","date":"2023-10-28","arxiv_id":"2310.18562","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/optimization-free-test-time-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2310.18562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18562"}},"official":{"repos":["Claydon-Wang/OFTTA"],"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","unlocated"]}}},{"url":"/paper/finding-order-in-chaos-a-novel-data-1","slug":"finding-order-in-chaos-a-novel-data-1","title":"Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning","date":"2023-09-23","arxiv_id":"2309.13439","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/finding-order-in-chaos-a-novel-data-1#ran","syntology_url":"https://syntology.ai/paper/2309.13439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13439"}},"official":{"repos":["eth-siplab/Finding_Order_in_Chaos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/human-activity-segmentation-challenge-ecml","slug":"human-activity-segmentation-challenge-ecml","title":"Human Activity Segmentation Challenge @ ECML/PKDD’23","date":"2023-09-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hard-no-box-adversarial-attack-on-skeleton","slug":"hard-no-box-adversarial-attack-on-skeleton","title":"Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient","date":"2023-08-10","arxiv_id":"2308.05681","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/hard-no-box-adversarial-attack-on-skeleton#ran","syntology_url":"https://syntology.ai/paper/2308.05681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05681"}},"official":{"repos":["luyg45/hardnoboxattack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-spiking-neural-network-on","slug":"evaluating-spiking-neural-network-on","title":"Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition","date":"2023-08-01","arxiv_id":"2308.00787","repositories_listed":1,"syntology":null},{"url":"/paper/mydigitalfootprint-an-extensive-context","slug":"mydigitalfootprint-an-extensive-context","title":"MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge","date":"2023-06-28","arxiv_id":"2306.15990","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-attention-based-unrolling-for-sparse","slug":"enhanced-attention-based-unrolling-for-sparse","title":"Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction","date":"2023-06-25","arxiv_id":"2306.14233","repositories_listed":1,"syntology":null},{"url":"/paper/multiwave-multiresolution-deep-architectures","slug":"multiwave-multiresolution-deep-architectures","title":"MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction","date":"2023-06-16","arxiv_id":"2306.10164","repositories_listed":1,"syntology":null},{"url":"/paper/vision-language-models-can-identify","slug":"vision-language-models-can-identify","title":"Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos","date":"2023-06-16","arxiv_id":"2306.10159","repositories_listed":1,"syntology":null},{"url":"/paper/ts-moco-time-series-momentum-contrast-for","slug":"ts-moco-time-series-momentum-contrast-for","title":"TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning","date":"2023-06-10","arxiv_id":"2306.06522","repositories_listed":1,"syntology":null},{"url":"/paper/amee-a-robust-framework-for-explanation","slug":"amee-a-robust-framework-for-explanation","title":"Robust Explainer Recommendation for Time Series Classification","date":"2023-06-08","arxiv_id":"2306.05501","repositories_listed":1,"syntology":null},{"url":"/paper/generalizable-low-resource-activity","slug":"generalizable-low-resource-activity","title":"Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning","date":"2023-05-25","arxiv_id":"2306.04641","repositories_listed":1,"syntology":null},{"url":"/paper/convboost-boosting-convnets-for-sensor-based","slug":"convboost-boosting-convnets-for-sensor-based","title":"ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition","date":"2023-05-22","arxiv_id":"2305.13541","repositories_listed":1,"syntology":null},{"url":"/paper/hang-time-har-a-benchmark-dataset-for","slug":"hang-time-har-a-benchmark-dataset-for","title":"Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors","date":"2023-05-22","arxiv_id":"2305.13124","repositories_listed":1,"syntology":null},{"url":"/paper/human-skeletons-and-change-detection-for","slug":"human-skeletons-and-change-detection-for","title":"Human skeletons and change detection for efficient violence detection in surveillance videos","date":"2023-05-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-matter-of-annotation-an-empirical-study-on","slug":"a-matter-of-annotation-an-empirical-study-on","title":"A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors","date":"2023-05-15","arxiv_id":"2305.08752","repositories_listed":1,"syntology":null},{"url":"/paper/generating-virtual-on-body-accelerometer-data","slug":"generating-virtual-on-body-accelerometer-data","title":"Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition","date":"2023-05-04","arxiv_id":"2305.03187","repositories_listed":1,"syntology":null},{"url":"/paper/big-little-adaptive-neural-networks-on-low","slug":"big-little-adaptive-neural-networks-on-low","title":"Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors","date":"2023-04-19","arxiv_id":"2304.09695","repositories_listed":1,"syntology":null},{"url":"/paper/hard-regularization-to-prevent-collapse-in","slug":"hard-regularization-to-prevent-collapse-in","title":"Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation","date":"2023-03-29","arxiv_id":"2303.16521","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-segmentation-applied-to-a-new","slug":"time-series-segmentation-applied-to-a-new","title":"Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities","date":"2023-03-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vidimu-multimodal-video-and-imu-kinematic","slug":"vidimu-multimodal-video-and-imu-kinematic","title":"Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)","date":"2023-03-27","arxiv_id":"2303.16150","repositories_listed":1,"syntology":null},{"url":"/paper/dual-path-adaptation-from-image-to-video","slug":"dual-path-adaptation-from-image-to-video","title":"Dual-path Adaptation from Image to Video Transformers","date":"2023-03-17","arxiv_id":"2303.09857","repositories_listed":1,"syntology":null},{"url":"/paper/decompl-decompositional-learning-with","slug":"decompl-decompositional-learning-with","title":"DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image","date":"2023-03-11","arxiv_id":"2303.06439","repositories_listed":1,"syntology":null},{"url":"/paper/group-activity-recognition-using-self","slug":"group-activity-recognition-using-self","title":"SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition","date":"2023-03-06","arxiv_id":"2303.12149","repositories_listed":1,"syntology":null},{"url":"/paper/musclemap-towards-video-based-activated","slug":"musclemap-towards-video-based-activated","title":"Towards Activated Muscle Group Estimation in the Wild","date":"2023-03-02","arxiv_id":"2303.00952","repositories_listed":1,"syntology":null},{"url":"/paper/towards-multi-user-activity-recognition","slug":"towards-multi-user-activity-recognition","title":"Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications","date":"2023-02-11","arxiv_id":"2302.05763","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-time-series-classification-2","slug":"deep-learning-for-time-series-classification-2","title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","date":"2023-02-06","arxiv_id":"2302.02515","repositories_listed":1,"syntology":null},{"url":"/paper/lampp-language-models-as-probabilistic-priors","slug":"lampp-language-models-as-probabilistic-priors","title":"LaMPP: Language Models as Probabilistic Priors for Perception and Action","date":"2023-02-03","arxiv_id":"2302.02801","repositories_listed":1,"syntology":null},{"url":"/paper/towards-continual-egocentric-activity","slug":"towards-continual-egocentric-activity","title":"Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning","date":"2023-01-26","arxiv_id":"2301.10931","repositories_listed":1,"syntology":null},{"url":"/paper/human-activity-recognition-in-an-open-world","slug":"human-activity-recognition-in-an-open-world","title":"Human Activity Recognition in an Open World","date":"2022-12-23","arxiv_id":"2212.12141","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-ppg-representation-learning","slug":"self-supervised-ppg-representation-learning","title":"Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability","date":"2022-12-07","arxiv_id":"2212.04902","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-ppg-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2212.04902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.04902"}},"official":{"repos":["Raminghorbanii/Self-Supervised-PPG-Representation-Learning-Shows-High-Inter-Subject-Variability"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/moma-lrg-language-refined-graphs-for-multi","slug":"moma-lrg-language-refined-graphs-for-multi","title":"MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing","date":"2022-11-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/swl-adapt-an-unsupervised-domain-adaptation","slug":"swl-adapt-an-unsupervised-domain-adaptation","title":"SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition","date":"2022-11-25","arxiv_id":"2212.00724","repositories_listed":1,"syntology":null},{"url":"/paper/wearable-based-human-activity-recognition","slug":"wearable-based-human-activity-recognition","title":"Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks","date":"2022-11-14","arxiv_id":"2212.02233","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/wearable-based-human-activity-recognition#ran","syntology_url":"https://syntology.ai/paper/2212.02233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02233"}},"official":{"repos":["intelligent-computing-lab-yale/snn_har"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/primask-cascadable-and-collusion-resilient","slug":"primask-cascadable-and-collusion-resilient","title":"PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference","date":"2022-11-12","arxiv_id":"2211.06716","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-enhancements-to-contrastive","slug":"investigating-enhancements-to-contrastive","title":"Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition","date":"2022-11-11","arxiv_id":"2211.06173","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-deep-learning-based-clustering","slug":"unsupervised-deep-learning-based-clustering","title":"Unsupervised Deep Learning-based clustering for Human Activity Recognition","date":"2022-11-10","arxiv_id":"2211.05483","repositories_listed":1,"syntology":null},{"url":"/paper/the-contribution-of-human-body-capacitance","slug":"the-contribution-of-human-body-capacitance","title":"The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition","date":"2022-10-26","arxiv_id":"2210.14794","repositories_listed":1,"syntology":null},{"url":"/paper/mmtsa-multimodal-temporal-segment-attention","slug":"mmtsa-multimodal-temporal-segment-attention","title":"MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition","date":"2022-10-14","arxiv_id":"2210.09222","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-feature-alignment-in-contrastive","slug":"temporal-feature-alignment-in-contrastive","title":"Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition","date":"2022-10-07","arxiv_id":"2210.03382","repositories_listed":1,"syntology":null},{"url":"/paper/radacs-towards-higher-order-reasoning-using","slug":"radacs-towards-higher-order-reasoning-using","title":"RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow","date":"2022-09-28","arxiv_id":"2209.14408","repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-transformers-for-human-activity","slug":"lightweight-transformers-for-human-activity","title":"Lightweight Transformers for Human Activity Recognition on Mobile Devices","date":"2022-09-22","arxiv_id":"2209.11750","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-representations-learning-for-time","slug":"generalized-representations-learning-for-time","title":"Out-of-Distribution Representation Learning for Time Series Classification","date":"2022-09-15","arxiv_id":"2209.07027","repositories_listed":1,"syntology":null},{"url":"/paper/alignment-based-conformance-checking-over","slug":"alignment-based-conformance-checking-over","title":"Alignment-based conformance checking over probabilistic events","date":"2022-09-09","arxiv_id":"2209.04309","repositories_listed":1,"syntology":null},{"url":"/paper/tfusion-transformer-based-n-to-one-multimodal","slug":"tfusion-transformer-based-n-to-one-multimodal","title":"SFusion: Self-attention based N-to-One Multimodal Fusion Block","date":"2022-08-26","arxiv_id":"2208.12776","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-egocentric-hand-object","slug":"fine-grained-egocentric-hand-object","title":"Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications","date":"2022-08-07","arxiv_id":"2208.03826","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-generation-of-novel-action","slug":"multimodal-generation-of-novel-action","title":"Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living","date":"2022-08-03","arxiv_id":"2208.01910","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-for-activity","slug":"domain-generalization-for-activity","title":"Domain Generalization for Activity Recognition via Adaptive Feature Fusion","date":"2022-07-21","arxiv_id":"2207.11221","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-contrastive-pre-training-for","slug":"self-supervised-contrastive-pre-training-for","title":"Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency","date":"2022-06-17","arxiv_id":"2206.08496","repositories_listed":1,"syntology":null},{"url":"/paper/proactive-self-attentive-temporal-point","slug":"proactive-self-attentive-temporal-point","title":"ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences","date":"2022-06-10","arxiv_id":"2206.05291","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":0,"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/proactive-self-attentive-temporal-point#ran","syntology_url":"https://syntology.ai/paper/2206.05291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05291"}},"official":{"repos":["data-iitd/proactive"],"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"]}}},{"url":"/paper/self-supervised-learning-for-human-activity","slug":"self-supervised-learning-for-human-activity","title":"Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data","date":"2022-06-06","arxiv_id":"2206.02909","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-for-human-activity#ran","syntology_url":"https://syntology.ai/paper/2206.02909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02909"}},"official":{"repos":["OxWearables/ssl-wearables"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ultra-compact-binary-neural-networks-for","slug":"ultra-compact-binary-neural-networks-for","title":"Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors","date":"2022-05-25","arxiv_id":"2205.12781","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-with-cross-modal","slug":"contrastive-learning-with-cross-modal","title":"Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition","date":"2022-05-20","arxiv_id":"2205.10071","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-diverse-hypotheses-and-knowledge","slug":"ensemble-diverse-hypotheses-and-knowledge","title":"Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation","date":"2022-04-15","arxiv_id":"2204.07308","repositories_listed":1,"syntology":null},{"url":"/paper/autofi-towards-automatic-wifi-human-sensing","slug":"autofi-towards-automatic-wifi-human-sensing","title":"AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning","date":"2022-04-12","arxiv_id":"2205.01629","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-transformer-for-nursing-activity","slug":"multimodal-transformer-for-nursing-activity","title":"Multimodal Transformer for Nursing Activity Recognition","date":"2022-04-09","arxiv_id":"2204.04564","repositories_listed":1,"syntology":null},{"url":"/paper/spact-self-supervised-privacy-preservation","slug":"spact-self-supervised-privacy-preservation","title":"SPAct: Self-supervised Privacy Preservation for Action Recognition","date":"2022-03-29","arxiv_id":"2203.15205","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/spact-self-supervised-privacy-preservation#ran","syntology_url":"https://syntology.ai/paper/2203.15205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15205"}},"official":{"repos":["daveishan/spact"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/audio-adaptive-activity-recognition-across","slug":"audio-adaptive-activity-recognition-across","title":"Audio-Adaptive Activity Recognition Across Video Domains","date":"2022-03-27","arxiv_id":"2203.14240","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-mining-with-scene-text-for-fine","slug":"knowledge-mining-with-scene-text-for-fine","title":"Knowledge Mining with Scene Text for Fine-Grained Recognition","date":"2022-03-27","arxiv_id":"2203.14215","repositories_listed":1,"syntology":null},{"url":"/paper/bridge-prompt-towards-ordinal-action","slug":"bridge-prompt-towards-ordinal-action","title":"Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos","date":"2022-03-26","arxiv_id":"2203.14104","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/bridge-prompt-towards-ordinal-action#ran","syntology_url":"https://syntology.ai/paper/2203.14104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14104"}},"official":{"repos":["ttlmh/bridge-prompt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fourier-disentangled-space-time-attention-for","slug":"fourier-disentangled-space-time-attention-for","title":"FAR: Fourier Aerial Video Recognition","date":"2022-03-21","arxiv_id":"2203.10694","repositories_listed":1,"syntology":null},{"url":"/paper/semipfl-personalized-semi-supervised","slug":"semipfl-personalized-semi-supervised","title":"SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence","date":"2022-03-15","arxiv_id":"2203.08176","repositories_listed":1,"syntology":null},{"url":"/paper/easy-ensemble-simple-deep-ensemble-learning","slug":"easy-ensemble-simple-deep-ensemble-learning","title":"Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition","date":"2022-03-08","arxiv_id":"2203.04153","repositories_listed":1,"syntology":null},{"url":"/paper/panoramic-human-activity-recognition","slug":"panoramic-human-activity-recognition","title":"Panoramic Human Activity Recognition","date":"2022-03-08","arxiv_id":"2203.03806","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":1,"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/panoramic-human-activity-recognition#ran","syntology_url":"https://syntology.ai/paper/2203.03806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03806"}},"official":{"repos":["ruizehan/par"],"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/har-gcnn-deep-graph-cnns-for-human-activity","slug":"har-gcnn-deep-graph-cnns-for-human-activity","title":"HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data","date":"2022-03-07","arxiv_id":"2203.03087","repositories_listed":1,"syntology":null},{"url":"/paper/transdarc-transformer-based-driver-activity","slug":"transdarc-transformer-based-driver-activity","title":"TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration","date":"2022-03-02","arxiv_id":"2203.00927","repositories_listed":1,"syntology":null}],"record_sha256":"70be707c5c81feafdbbdac69f278ef2914ed0b6b38d51a2378258706a1b37921","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}