{"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/11","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":11,"pages_in_order":14,"rows_per_page":100,"rows":[1001,1100],"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/papers/10","next":"/task/activity-recognition/papers/12","papers":[{"url":null,"slug":"group-activity-detection-from-trajectory-and","title":"Group Activity Detection from Trajectory and Video Data in Soccer","date":"2020-04-21","arxiv_id":"2004.10299","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-inertial","title":"Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey","date":"2020-04-19","arxiv_id":"2004.08821","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-unet-a-condition-aware-deep-model","title":"Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables","date":"2020-04-15","arxiv_id":"2004.09376","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-motion-relevance-for-activity","title":"Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models","date":"2020-03-31","arxiv_id":"2003.14285","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimised-convolutional-neural-networks-for","title":"Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications","date":"2020-03-30","arxiv_id":"2004.00505","repositories_listed":0,"syntology":null},{"url":null,"slug":"actor-transformers-for-group-activity","title":"Actor-Transformers for Group Activity Recognition","date":"2020-03-28","arxiv_id":"2003.12737","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-learning-from-human-pose-and","title":"Simultaneous Learning from Human Pose and Object Cues for Real-Time Activity Recognition","date":"2020-03-26","arxiv_id":"2004.03453","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-transferability-in-wearable","title":"Adversarial Transferability in Wearable Sensor Systems","date":"2020-03-17","arxiv_id":"2003.07982","repositories_listed":0,"syntology":null},{"url":null,"slug":"actilabel-a-combinatorial-transfer-learning","title":"ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition","date":"2020-03-16","arxiv_id":"2003.07415","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-guest-detection-in-a-smart-home-using","title":"Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning","date":"2020-03-13","arxiv_id":"2003.06347","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fourier-domain-feature-approach-for-human","title":"A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection","date":"2020-03-11","arxiv_id":"2003.05209","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-maximum-likelihood-approach-to-speed","title":"Maximum Likelihood Speed Estimation of Moving Objects in Video Signals","date":"2020-03-10","arxiv_id":"2003.04883","repositories_listed":0,"syntology":null},{"url":"/paper/group-activity-recognition-by-using-effective","slug":"group-activity-recognition-by-using-effective","title":"Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention","date":"2020-03-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-learning-for-multi-modal-video","title":"Cross-modal Learning for Multi-modal Video Categorization","date":"2020-03-07","arxiv_id":"2003.03501","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-method-for-complex-human","title":"A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors","date":"2020-03-04","arxiv_id":"2003.01874","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-deep-context","title":"Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery","date":"2020-03-03","arxiv_id":"2003.01753","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-federated-learning-for","title":"Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework","date":"2020-02-25","arxiv_id":"2002.10671","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-class-boundary-label-uncertainty","title":"Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance","date":"2020-02-23","arxiv_id":"2002.09963","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-stream-fusion-network-for-first-person","title":"Three-Stream Fusion Network for First-Person Interaction Recognition","date":"2020-02-19","arxiv_id":"2002.08219","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-rich-sampling-technique-over","title":"An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos","date":"2020-02-06","arxiv_id":"2002.02100","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-the-utilization-of-public-open","title":"Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront","date":"2020-02-04","arxiv_id":"2002.01461","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-prediction-in-time-series-data","title":"Multi-label Prediction in Time Series Data using Deep Neural Networks","date":"2020-01-27","arxiv_id":"2001.10098","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-accelerometers-for-activity-recognition-a","title":"Are Accelerometers for Activity Recognition a Dead-end?","date":"2020-01-22","arxiv_id":"2001.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-activity-recognition-with-videos","title":"Zero-Shot Activity Recognition with Videos","date":"2020-01-22","arxiv_id":"2002.02265","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-sensor-based-human-activity","title":"Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities","date":"2020-01-21","arxiv_id":"2001.07416","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-classification-using-kinematically","title":"Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures","date":"2020-01-19","arxiv_id":"2001.08582","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generalizable-surgical-activity","title":"Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks","date":"2020-01-11","arxiv_id":"2001.03728","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-human-activity-recognition","title":"Classification of human activity recognition using smartphones","date":"2020-01-06","arxiv_id":"2001.09740","repositories_listed":0,"syntology":null},{"url":null,"slug":"bonn-activity-maps-dataset-description","title":"Bonn Activity Maps: Dataset Description","date":"2019-12-13","arxiv_id":"1912.06354","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-machine-learning-across","title":"Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders","date":"2019-12-12","arxiv_id":"1912.05879","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-recognition-of-complex-action","title":"DASZL: Dynamic Action Signatures for Zero-shot Learning","date":"2019-12-08","arxiv_id":"1912.03613","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-model-featuring-cnn-and-lstm","title":"Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data","date":"2019-12-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"template-co-updating-in-multi-modal-human","title":"Template co-updating in multi-modal human activity recognition systems","date":"2019-12-04","arxiv_id":"1912.02024","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsa-randomized-simulation-as-augmentation-for","title":"RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition","date":"2019-12-03","arxiv_id":"1912.01180","repositories_listed":0,"syntology":null},{"url":null,"slug":"skeleton-based-activity-recognition-by-fusing","title":"Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues","date":"2019-12-02","arxiv_id":"1912.00576","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transfer-learning-method-for-goal","title":"A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features","date":"2019-11-22","arxiv_id":"1911.10134","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusion-of-deep-neural-networks-for-activity","title":"Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach","date":"2019-11-21","arxiv_id":"1905.02703","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-implementation-features","title":"Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition","date":"2019-11-21","arxiv_id":"1911.09325","repositories_listed":0,"syntology":null},{"url":null,"slug":"activity-monitoring-of-islamic-prayer-salat","title":"Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning","date":"2019-11-11","arxiv_id":"1911.04102","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013401","title":"Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing","date":"2019-10-29","arxiv_id":"1910.13401","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-of-human-activity","title":"A systematic review of smartphone-based human activity recognition for health research","date":"2019-10-07","arxiv_id":"1910.03970","repositories_listed":0,"syntology":null},{"url":"/paper/driveact-a-multi-modal-dataset-for-fine","slug":"driveact-a-multi-modal-dataset-for-fine","title":"Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"new-convex-relaxations-for-mrf-inference-with","title":"New Convex Relaxations for MRF Inference With Unknown Graphs","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/toyota-smarthome-real-world-activities-of","slug":"toyota-smarthome-real-world-activities-of","title":"Toyota Smarthome: Real-World Activities of Daily Living","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-audiovisual-activity","title":"Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-generative-adversarial","title":"Generating Fair Universal Representations using Adversarial Models","date":"2019-09-27","arxiv_id":"1910.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"sign-language-recognition-analysis-using","title":"Sign Language Recognition Analysis using Multimodal Data","date":"2019-09-24","arxiv_id":"1909.11232","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lightweight-deep-learning-model-for-human","title":"A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices","date":"2019-09-20","arxiv_id":"1909.12917","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-segmentation-and-recognition","title":"Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition","date":"2019-09-18","arxiv_id":"1909.08606","repositories_listed":0,"syntology":null},{"url":"/paper/activity-recognition-using-st-gcn-with-3d","slug":"activity-recognition-using-st-gcn-with-3d","title":"Activity recognition using ST-GCN with 3D motion data","date":"2019-09-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-few-shot-time-series","title":"Meta-Learning for Few-Shot Time Series Classification","date":"2019-09-13","arxiv_id":"1909.07155","repositories_listed":0,"syntology":null},{"url":"/paper/can-a-simple-approach-identify-complex-nurse","slug":"can-a-simple-approach-identify-complex-nurse","title":"Can a simple approach identify complex nurse care activity?","date":"2019-09-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-low-resolution-activity-recognition-1","title":"Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer","date":"2019-09-09","arxiv_id":"1909.03580","repositories_listed":0,"syntology":null},{"url":null,"slug":"nurse-care-activity-recognition-challenge","title":"Nurse care activity recognition challenge: summary and results","date":"2019-09-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"personalizing-smartwatch-based-activity","title":"Personalizing Smartwatch Based Activity Recognition Using Transfer Learning","date":"2019-09-03","arxiv_id":"1909.01202","repositories_listed":0,"syntology":null},{"url":"/paper/temporal-reasoning-graph-for-activity","slug":"temporal-reasoning-graph-for-activity","title":"Temporal Reasoning Graph for Activity Recognition","date":"2019-08-27","arxiv_id":"1908.09995","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807519","title":"Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing","date":"2019-08-20","arxiv_id":"1908.07519","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-feature-selection-for-activity","title":"Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback","date":"2019-08-16","arxiv_id":"1908.06134","repositories_listed":0,"syntology":null},{"url":null,"slug":"wi-fringe-leveraging-text-semantics-in-wifi","title":"Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition","date":"2019-08-16","arxiv_id":"1908.06803","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-branches-detecting-actions-with-richer","title":"Three Branches: Detecting Actions With Richer Features","date":"2019-08-13","arxiv_id":"1908.04519","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-relation-learning-for-group","title":"Progressive Relation Learning for Group Activity Recognition","date":"2019-08-08","arxiv_id":"1908.02948","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-self-supervised-learning-for-human","title":"Multi-task Self-Supervised Learning for Human Activity Detection","date":"2019-07-27","arxiv_id":"1907.11879","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-human-association-between-top-and","title":"Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions","date":"2019-07-26","arxiv_id":"1907.11458","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedhealth-a-federated-transfer-learning","title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","date":"2019-07-22","arxiv_id":"1907.09173","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-surgical-activity-recognition-with","title":"Automated Surgical Activity Recognition with One Labeled Sequence","date":"2019-07-20","arxiv_id":"1907.08825","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-deep-neural-network-applied-to","title":"An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing","date":"2019-07-17","arxiv_id":"1907.09523","repositories_listed":0,"syntology":null},{"url":null,"slug":"avd-adversarial-video-distillation","title":"AVD: Adversarial Video Distillation","date":"2019-07-12","arxiv_id":"1907.05640","repositories_listed":0,"syntology":null},{"url":null,"slug":"tweets-can-tell-activity-recognition-using","title":"Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model","date":"2019-07-10","arxiv_id":"1908.02551","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-eye-gaze-tracking-and-its","title":"Image based Eye Gaze Tracking and its Applications","date":"2019-07-09","arxiv_id":"1907.04325","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-computing-in-wearable","title":"Resource-Efficient Computing in Wearable Systems","date":"2019-07-07","arxiv_id":"1907.03247","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-wearable-computing-for","title":"Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification","date":"2019-07-07","arxiv_id":"1907.03250","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-evaluation-of-surgical-activity","title":"Novel evaluation of surgical activity recognition models using task-based efficiency metrics","date":"2019-07-03","arxiv_id":"1907.02060","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-body-parts-tracking-applications-to","title":"Human Body Parts Tracking: Applications to Activity Recognition","date":"2019-07-02","arxiv_id":"1907.05281","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-identifying-group-behavior-of","title":"A Framework For Identifying Group Behavior Of Wild Animals","date":"2019-07-01","arxiv_id":"1907.00932","repositories_listed":0,"syntology":null},{"url":null,"slug":"different-approaches-for-human-activity","title":"Different Approaches for Human Activity Recognition: A Survey","date":"2019-06-11","arxiv_id":"1906.05074","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-driven-active-and-incremental","title":"Context-driven Active and Incremental Activity Recognition","date":"2019-06-07","arxiv_id":"1906.03033","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsesense-human-activity-recognition-from","title":"SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks","date":"2019-06-06","arxiv_id":"1906.02399","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-activity-recognition-of","title":"Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach","date":"2019-06-05","arxiv_id":"1906.02070","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-user-independent-to-personal-human","title":"From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone","date":"2019-05-29","arxiv_id":"1905.12285","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalizing-human-activity-recognition","title":"Personalizing human activity recognition models using incremental learning","date":"2019-05-29","arxiv_id":"1905.12628","repositories_listed":0,"syntology":null},{"url":null,"slug":"importance-of-user-inputs-while-using","title":"Importance of user inputs while using incremental learning to personalize human activity recognition models","date":"2019-05-28","arxiv_id":"1905.11775","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-attentional-activity-recognition","title":"Multi-agent Attentional Activity Recognition","date":"2019-05-22","arxiv_id":"1905.08948","repositories_listed":0,"syntology":null},{"url":null,"slug":"activity-recognition-and-prediction-in-real","title":"Activity Recognition and Prediction in Real Homes","date":"2019-05-20","arxiv_id":"1905.08654","repositories_listed":0,"syntology":null},{"url":null,"slug":"disparity-augmented-trajectories-for-human","title":"Disparity-Augmented Trajectories for Human Activity Recognition","date":"2019-05-14","arxiv_id":"1905.05344","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-multi-task-hierarchical-attention","title":"Federated Multi-task Hierarchical Attention Model for Sensor Analytics","date":"2019-05-13","arxiv_id":"1905.05142","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-flow-profile-image-for-video","title":"On Flow Profile Image for Video Representation","date":"2019-05-12","arxiv_id":"1905.04668","repositories_listed":0,"syntology":null},{"url":null,"slug":"follow-the-attention-combining-partial-pose","title":"Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection","date":"2019-05-11","arxiv_id":"1905.04430","repositories_listed":0,"syntology":null},{"url":null,"slug":"190504293","title":"Differential Recurrent Neural Network and its Application for Human Activity Recognition","date":"2019-05-09","arxiv_id":"1905.04293","repositories_listed":0,"syntology":null},{"url":null,"slug":"wearable-sensor-data-based-human-activity","title":"Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach","date":"2019-05-09","arxiv_id":"1905.03809","repositories_listed":0,"syntology":null},{"url":null,"slug":"190503707","title":"Human Activity Recognition Using Visual Object Detection","date":"2019-05-02","arxiv_id":"1905.03707","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-lstm-rnn","title":"Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture","date":"2019-05-02","arxiv_id":"1905.00599","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramid-recurrent-neural-networks-for-multi","title":"Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmented-convolutional-gated-recurrent","title":"Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar","date":"2019-04-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-variable-algorithms-for-multimodal","title":"Latent Variable Algorithms for Multimodal Learning and Sensor Fusion","date":"2019-04-23","arxiv_id":"1904.10450","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-first-person-activity","title":"Semi-Supervised First-Person Activity Recognition in Body-Worn Video","date":"2019-04-19","arxiv_id":"1904.09062","repositories_listed":0,"syntology":null},{"url":null,"slug":"190411882","title":"Smart Laptop Bag with Machine Learning for Activity Recognition","date":"2019-04-14","arxiv_id":"1904.11882","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-synthesis-of-anomalies-in-videos","title":"Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal","date":"2019-04-14","arxiv_id":"1904.06633","repositories_listed":0,"syntology":null},{"url":null,"slug":"digging-deeper-into-egocentric-gaze","title":"Digging Deeper into Egocentric Gaze Prediction","date":"2019-04-12","arxiv_id":"1904.06090","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-query-selection-for-active","title":"Context-Aware Query Selection for Active Learning in Event Recognition","date":"2019-04-09","arxiv_id":"1904.04406","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-relational-machine-for-group","title":"Convolutional Relational Machine for Group Activity Recognition","date":"2019-04-05","arxiv_id":"1904.03308","repositories_listed":0,"syntology":null}],"record_sha256":"c6ae103511d135cb98af475e3a128418523a21fe458193f6b350f9c79c544070","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}