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Vision2Sensor
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_2openalex-snapshot
SSD: Single Shot MultiBox Detector
inferredcites →https://doi.org/10.1007/978-3-642-21257-4_36openalex-snapshot
Human Activity Recognition from Accelerometer Data Using a Wearable Device
inferredcites →https://doi.org/10.1007/s10115-013-0665-3openalex-snapshot
Transfer learning for activity recognition: a survey
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.eswa.2016.04.032openalex-snapshot
Human activity recognition with smartphone sensors using deep learning neural networks
inferredcites →https://doi.org/10.1016/j.pmcj.2011.06.004openalex-snapshot
Centinela: A human activity recognition system based on acceleration and vital sign data
inferredcites →https://doi.org/10.1016/j.rehab.2018.05.067openalex-snapshot
Wristband accelerometers to motivate arm exercise after stroke (WAVES): Activity data from a pilot randomised controlled trial
inferredcites →https://doi.org/10.1109/72.963764openalex-snapshot
Confidence estimation methods for neural networks: a practical comparison
inferredcites →https://doi.org/10.1109/cvpr.2014.222openalex-snapshot
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.223openalex-snapshot
Large-Scale Video Classification with Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.502openalex-snapshot
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
inferredcites →https://doi.org/10.1109/iccv.2015.510openalex-snapshot
Learning Spatiotemporal Features with 3D Convolutional Networks
inferredcites →https://doi.org/10.1109/infcom.2000.832252openalex-snapshot
RADAR: an in-building RF-based user location and tracking system
inferredcites →https://doi.org/10.1109/ipin.2013.6817916openalex-snapshot
HiMLoc: Indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting
inferredcites →https://doi.org/10.1109/ipin.2014.7275483openalex-snapshot
Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement
inferredcites →https://doi.org/10.1109/iswc.2012.15openalex-snapshot
Automatic Synchronization of Wearable Sensors and Video-Cameras for Ground Truth Annotation -- A Practical Approach
inferredcites →https://doi.org/10.1109/spcom.2018.8724461openalex-snapshot
Estimating Confidence for Deep Neural Networks through Density modeling
inferredcites →https://doi.org/10.1109/surv.2012.110112.00192openalex-snapshot
A Survey on Human Activity Recognition using Wearable Sensors
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1145/1859995.1860016openalex-snapshot
Indoor localization without the pain
inferredcites →https://doi.org/10.1145/2968219.2971461openalex-snapshot
Towards multimodal deep learning for activity recognition on mobile devices
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3090076openalex-snapshot
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
inferredcites →https://doi.org/10.1145/3161174openalex-snapshot
Multimodal Deep Learning for Activity and Context Recognition
inferredcites →https://doi.org/10.1145/3161201openalex-snapshot
An LSTM Based System for Prediction of Human Activities with Durations
inferredcites →https://doi.org/10.1145/3290605.3300778openalex-snapshot
Enabling Identification and Behavioral Sensing in Homes using Radio Reflections
inferredcites →https://doi.org/10.14722/ndss.2014.23059openalex-snapshot
AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable
inferredcites →https://doi.org/10.3390/s150102059openalex-snapshot
A Survey of Online Activity Recognition Using Mobile Phones
inferredcites →https://doi.org/10.4108/icst.pervasivehealth.2012.248689openalex-snapshot
Multi-Modal Mobile Sensing of Social Interactions
inferredcites →https://doi.org/10.48550/arxiv.1212.0402openalex-snapshot
UCF-101: A dataset of 101 human actions classes from videos in the wild
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1411.1792openalex-snapshot
How transferable are features in deep neural networks?
inferredcites →https://doi.org/10.48550/arxiv.1703.02910openalex-snapshot
Deep Bayesian Active Learning with Image Data
inferredcites →https://doi.org/10.48550/arxiv.1704.00109openalex-snapshot
Snapshot Ensembles: Train 1, get M for free
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1706.04599openalex-snapshot
On Calibration of Modern Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1707.07013openalex-snapshot
Confidence estimation in Deep Neural networks via density modelling
inferredcites →https://doi.org/10.48550/arxiv.1807.05284openalex-snapshot
Survey on Deep Learning Techniques for Person Re-Identification Task
inferredcites →https://doi.org/10.5555/2602339.2602355openalex-snapshot
Lightweight map matching for indoor localisation using conditional random fields
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-54528-3_11openalex-snapshot
STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar
inferred← citeshttps://doi.org/10.1007/978-981-15-8944-7_1openalex-snapshot
Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition
inferred← citeshttps://doi.org/10.1007/s10462-022-10141-4openalex-snapshot
AI on the edge: a comprehensive review
inferred← citeshttps://doi.org/10.1007/s42486-025-00205-zopenalex-snapshot
Synthesizing mmWave range-doppler data from videos for privacy-preserving human activity recognition
inferred← citeshttps://doi.org/10.1016/j.inffus.2021.10.009openalex-snapshot
Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition
inferred← citeshttps://doi.org/10.1038/s41598-025-00065-8openalex-snapshot
RaGeoSense for smart home gesture recognition using sparse millimeter wave radar point clouds
inferred← citeshttps://doi.org/10.1109/access.2025.3589938openalex-snapshot
Human Behavior Analysis: A Comprehensive Survey on Techniques, Applications, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.1109/icmeae58636.2022.00029openalex-snapshot
Human Activity Recognition implenting the Yolo models
inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11227338openalex-snapshot
An Effective GANs-Based Method for High-Information-Value Multimodal Wearable Sensor Data Synthesis
inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11228173openalex-snapshot
LSTPointGMN: Lightweight Spatial-Temporal Graph Mamba Network for Gesture Recognition Using Millimeter-Wave Radar
inferred← citeshttps://doi.org/10.1109/ipin.2019.8911770openalex-snapshot
CamLoc: Pedestrian Location Estimation through Body Pose Estimation on Smart Cameras
inferred← citeshttps://doi.org/10.1109/ipin51156.2021.9662543openalex-snapshot
Active Learning with Data Distribution Shift Detection for Updating Localization Systems
inferred← citeshttps://doi.org/10.1109/jsen.2023.3314728openalex-snapshot
Sign Language Recognition With Self-Learning Fusion Model
inferred← citeshttps://doi.org/10.1109/mnet.011.2100035openalex-snapshot
A Cross-Domain Augmentation-Based AI Learning Framework for In-Network Gesture Recognition
inferred← citeshttps://doi.org/10.1109/mprv.2021.3051869openalex-snapshot
Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts
inferred← citeshttps://doi.org/10.1109/tmc.2023.3279608openalex-snapshot
A Handwriting Recognition System With WiFi
inferred← citeshttps://doi.org/10.1109/tmc.2025.3549705openalex-snapshot
Federated Multi-Source Domain Adaptation for mmWave-Based Human Activity Recognition
inferred← citeshttps://doi.org/10.1109/tmc.2025.3556998openalex-snapshot
From Vision to Motion: Translating Large-Scale Knowledge for Data-Scarce IMU Applications
inferred← citeshttps://doi.org/10.1145/3397323openalex-snapshot
Adversarial Multi-view Networks for Activity Recognition
inferred← citeshttps://doi.org/10.1145/3410530.3414370openalex-snapshot
Improving activity data collection with on-device personalization using fine-tuning
inferred← citeshttps://doi.org/10.1145/3432235openalex-snapshot
Real-time Arm Gesture Recognition in Smart Home Scenarios via Millimeter Wave Sensing
inferred← citeshttps://doi.org/10.1145/3472290openalex-snapshot
A Survey on Deep Learning for Human Activity Recognition
inferred← citeshttps://doi.org/10.1145/3659597openalex-snapshot
CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining
inferred← citeshttps://doi.org/10.1145/3699776openalex-snapshot
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
inferred← citeshttps://doi.org/10.1145/3717608openalex-snapshot
Transfer Learning in Sensor-Based Human Activity Recognition: A Survey
inferred← citeshttps://doi.org/10.1145/3749509openalex-snapshot
One Model to Fit Them All: Universal IMU-based Human Activity Recognition with LLM-assisted Cross-dataset Representation
inferred← citeshttps://doi.org/10.1145/3832003openalex-snapshot
Adapting Pretrained Large Vision Models for Sensor-based Activity Recognition
inferred← citeshttps://doi.org/10.3390/app11073094openalex-snapshot
Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks
inferred← citeshttps://doi.org/10.3390/app11167660openalex-snapshot
The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
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