On the public citation graph
DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition
0 signed · 116 inferred · view as graph
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.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
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_26openalex-snapshot
Edge Boxes: Locating Object Proposals from Edges
inferredcites →https://doi.org/10.1007/978-3-540-79547-6_30openalex-snapshot
CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval
inferredcites →https://doi.org/10.1007/978-3-540-88688-4_52openalex-snapshot
Active Image Labeling and Its Application to Facial Action Labeling
inferredcites →https://doi.org/10.1007/978-3-662-44415-3_16openalex-snapshot
Majority Vote of Diverse Classifiers for Late Fusion
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1109/cvpr.2011.5995380openalex-snapshot
Learning structured prediction models for interactive image labeling
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccvw.2015.59openalex-snapshot
Recognizing Personal Contexts from Egocentric Images
inferredcites →https://doi.org/10.1109/iros.2008.4651113openalex-snapshot
Robots at home: Understanding long-term human-robot interaction
inferredcites →https://doi.org/10.1109/mprv.2009.82openalex-snapshot
The Case for VM-Based Cloudlets in Mobile Computing
inferredcites →https://doi.org/10.1109/msp.2014.2347059openalex-snapshot
Visual Domain Adaptation: A survey of recent advances
inferredcites →https://doi.org/10.1109/tpami.2012.100openalex-snapshot
Tree-Structured CRF Models for Interactive Image Labeling
inferredcites →https://doi.org/10.1109/wacv.2015.77openalex-snapshot
Deeply-Learned Feature for Age Estimation
inferredcites →https://doi.org/10.1137/1.9781611972801.23openalex-snapshot
Single-Pass Distributed Learning of Multi-Class SVMs using Core-Sets
inferredcites →https://doi.org/10.1145/1460096.1460165openalex-snapshot
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
inferredcites →https://doi.org/10.1145/1878961.1878982openalex-snapshot
Accurate online power estimation and automatic battery behavior based power model generation for smartphones
inferredcites →https://doi.org/10.1145/2342509.2342513openalex-snapshot
Fog computing and its role in the internet of things
inferredcites →https://doi.org/10.1145/2370216.2370288openalex-snapshot
Automatically characterizing places with opportunistic crowdsensing using smartphones
inferredcites →https://doi.org/10.1145/2647868.2654948openalex-snapshot
Deep Learning for Content-Based Image Retrieval
inferredcites →https://doi.org/10.1145/2699343.2699349openalex-snapshot
Can Deep Learning Revolutionize Mobile Sensing?
inferredcites →https://doi.org/10.1145/2802083.2808398openalex-snapshot
Predicting daily activities from egocentric images using deep learning
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/985921.986087openalex-snapshot
Robotic pets in the lives of preschool children
inferredcites →https://doi.org/10.4108/icst.mobicase.2014.257757openalex-snapshot
Cloudlets: at the Leading Edge of Mobile-Cloud Convergence
inferredcites →https://doi.org/10.48550/arxiv.1312.6204openalex-snapshot
One-Shot Adaptation of Supervised Deep Convolutional Models
inferredcites →https://doi.org/10.5555/1283383.1283494openalex-snapshot
k-means++: the advantages of careful seeding
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ett.4648openalex-snapshot
Memory optimization at Edge for Distributed Convolution Neural Network
inferred← citeshttps://doi.org/10.1007/978-3-030-15032-7_36openalex-snapshot
Accelerating the Digital Transformation of Business and Society Through Composite Business Ecosystems
inferred← citeshttps://doi.org/10.1007/978-3-031-54277-0_8openalex-snapshot
Embedded Computer Vision for Agricultural Applications
inferred← citeshttps://doi.org/10.1007/978-981-15-6186-3_4openalex-snapshot
Artificial Intelligence Applications on Edge
inferred← citeshttps://doi.org/10.1007/978-981-15-6186-3_7openalex-snapshot
Edge Computing for Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/s42045-018-0003-0openalex-snapshot
Exploiting the edge power: an edge deep learning framework
inferred← citeshttps://doi.org/10.1016/j.cag.2019.09.003openalex-snapshot
Geometrical and statistical incremental semantic modeling on mobile devices
inferred← citeshttps://doi.org/10.1016/j.comcom.2020.07.001openalex-snapshot
Learning distributed communication and computation in the IoT
inferred← citeshttps://doi.org/10.1016/j.future.2020.08.001openalex-snapshot
An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm
inferred← citeshttps://doi.org/10.1016/j.future.2022.07.023openalex-snapshot
An edge–cloud integrated framework for flexible and dynamic stream analytics
inferred← citeshttps://doi.org/10.1016/j.ins.2020.02.041openalex-snapshot
Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications
inferred← citeshttps://doi.org/10.1016/j.jclepro.2020.125401openalex-snapshot
IoT based intelligence for proactive waste management in Quick Service Restaurants
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.09.045openalex-snapshot
Distributing DNN training over IoT edge devices based on transfer learning
inferred← citeshttps://doi.org/10.1016/j.sysarc.2019.02.009openalex-snapshot
All one needs to know about fog computing and related edge computing paradigms: A complete survey
inferred← citeshttps://doi.org/10.1038/s42256-019-0134-0openalex-snapshot
Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence
inferred← citeshttps://doi.org/10.1049/cit2.12114openalex-snapshot
Scope of machine learning applications for addressing the challenges in next‐generation wireless networks
inferred← citeshttps://doi.org/10.1109/access.2019.2948399openalex-snapshot
What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges
inferred← citeshttps://doi.org/10.1109/access.2019.2958962openalex-snapshot
A Survey on Deep Learning Empowered IoT Applications
inferred← citeshttps://doi.org/10.1109/access.2020.2982411openalex-snapshot
Deep Learning for Edge Computing Applications: A State-of-the-Art Survey
inferred← citeshttps://doi.org/10.1109/access.2021.3058021openalex-snapshot
Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms
inferred← citeshttps://doi.org/10.1109/access.2022.3220694openalex-snapshot
A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges
inferred← citeshttps://doi.org/10.1109/access.2023.3347548openalex-snapshot
Near-Edge Computing Aware Object Detection: A Review
inferred← citeshttps://doi.org/10.1109/acit50332.2020.9300097openalex-snapshot
Practice of Applied Edge Analytics in Intelligent Learning Framework
inferred← citeshttps://doi.org/10.1109/ccdc62350.2024.10587654openalex-snapshot
Research on Aerial Photography Target Detection Methods
inferred← citeshttps://doi.org/10.1109/ccnc65079.2026.11366406openalex-snapshot
SCIoT: Design and Evaluation of a Split Computing Framework for Collaborative Inference in the IoT
inferred← citeshttps://doi.org/10.1109/cds52072.2021.00077openalex-snapshot
Autonomous Driving Task Offloading with Mobile Edge Computing
inferred← citeshttps://doi.org/10.1109/cits49457.2020.9232575openalex-snapshot
Edge Intelligence: Challenges and Opportunities
inferred← citeshttps://doi.org/10.1109/cloud.2018.00095openalex-snapshot
DRESS: Dynamic RESource-Reservation Scheme for Congested Data-Intensive Computing Platforms
inferred← citeshttps://doi.org/10.1109/cloudnet.2018.8549539openalex-snapshot
Demystifying Myths of MEC: Rethinking and Exploring Benefits of Multi-Access/Mobile Edge Computing
inferred← citeshttps://doi.org/10.1109/comapp.2018.8460239openalex-snapshot
A Bilingual Scene-to-Speech Mobile Based Application
inferred← citeshttps://doi.org/10.1109/comst.2018.2844341openalex-snapshot
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
inferred← citeshttps://doi.org/10.1109/comst.2019.2904897openalex-snapshot
Deep Learning in Mobile and Wireless Networking: A Survey
inferred← citeshttps://doi.org/10.1109/comst.2020.2970550openalex-snapshot
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/hpec.2018.8547530openalex-snapshot
A Novel 1D-Convolution Accelerator for Low-Power Real-time CNN processing on the Edge
inferred← citeshttps://doi.org/10.1109/icccn.2018.8487352openalex-snapshot
Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning
inferred← citeshttps://doi.org/10.1109/iccv.2017.161openalex-snapshot
Adaptive RNN Tree for Large-Scale Human Action Recognition
inferred← citeshttps://doi.org/10.1109/icii.2018.00019openalex-snapshot
An Edge Computing Framework for Real-Time Monitoring in Smart Grid
inferred← citeshttps://doi.org/10.1109/iciot48696.2020.9089557openalex-snapshot
A Scene-to-Speech Mobile based Application: Multiple Trained Models Approach
inferred← citeshttps://doi.org/10.1109/icmla.2017.0-169openalex-snapshot
Automatic Generation and Recommendation for API Mashups
inferred← citeshttps://doi.org/10.1109/icnp.2017.8117585openalex-snapshot
When deep learning meets edge computing
inferred← citeshttps://doi.org/10.1109/icoten52080.2021.9493514openalex-snapshot
GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide
inferred← citeshttps://doi.org/10.1109/isca59077.2024.00093openalex-snapshot
DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics
inferred← citeshttps://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00138openalex-snapshot
A Cascade Collaborative Offloading Framework for Video Analytics Based on Online Learning
inferred← citeshttps://doi.org/10.1109/jiot.2020.2990215openalex-snapshot
AWARE-CNN: Automated Workflow for Application-Aware Real-Time Edge Acceleration of CNNs
inferred← citeshttps://doi.org/10.1109/jiot.2024.3373028openalex-snapshot
A Lightweight Small Object Detection Method Based on Multilayer Coordination Federated Intelligence for Coal Mine IoVT
inferred← citeshttps://doi.org/10.1109/jproc.2019.2921977openalex-snapshot
Deep Learning With Edge Computing: A Review
inferred← citeshttps://doi.org/10.1109/jproc.2021.3119950openalex-snapshot
Edge Intelligence: Empowering Intelligence to the Edge of Network
inferred← citeshttps://doi.org/10.1109/jproc.2022.3226481openalex-snapshot
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
inferred← citeshttps://doi.org/10.1109/lcn53696.2022.9843735openalex-snapshot
MicroTL: Transfer Learning on Low-Power IoT Devices
inferred← citeshttps://doi.org/10.1109/padsw.2018.8644604openalex-snapshot
EdgeCNN: A Hybrid Architecture for Agile Learning of Healthcare Data from IoT Devices
inferred← citeshttps://doi.org/10.1109/percomworkshops68308.2026.11585345openalex-snapshot
Privacy-Preserving Multi-Layer Human Digital Twins with User-Controlled Data Management
inferred← citeshttps://doi.org/10.1109/sec.2018.00049openalex-snapshot
Privacy Partition: A Privacy-Preserving Framework for Deep Neural Networks in Edge Networks
inferred← citeshttps://doi.org/10.1109/sec.2018.00052openalex-snapshot
How Edge Computing and Initial Congestion Window Affect Latency of Web-Based Services: Early Experiences with Baidu?
inferred← citeshttps://doi.org/10.1109/sec62691.2024.00017openalex-snapshot
An Accurate and Efficient Clustered Federated Learning for Mobile Edge Devices
inferred← citeshttps://doi.org/10.1109/smartcomp50058.2020.00037openalex-snapshot
Deep Emergent Communication for the IoT
inferred← citeshttps://doi.org/10.1109/tcc.2020.2978846openalex-snapshot
Distributed and Collaborative High-Speed Inference Deep Learning for Mobile Edge with Topological Dependencies
inferred← citeshttps://doi.org/10.1109/tcc.2023.3294490openalex-snapshot
CLUE: Systems Support for Knowledge Transfer in Collaborative Learning With Neural Nets
inferred← citeshttps://doi.org/10.1109/tcc.2024.3351716openalex-snapshot
Locality-Aware and Fault-Tolerant Batching for Machine Learning on Distributed Datasets
inferred← citeshttps://doi.org/10.1109/tnse.2021.3101986openalex-snapshot
Optimising Cost vs Accuracy of Decentralised Analytics in Fog Computing Environments
inferred← citeshttps://doi.org/10.1109/wacv.2018.00046openalex-snapshot
ReHAR: Robust and Efficient Human Activity Recognition
inferred← citeshttps://doi.org/10.1109/wcnc45663.2020.9120786openalex-snapshot
MeFILL: A Multi-edged Framework for Intelligent and Low Latency Mobile IoT Services
inferred← citeshttps://doi.org/10.1109/wcncw.2019.8902527openalex-snapshot
Adaptive Task Allocation for Mobile Edge Learning
inferred← citeshttps://doi.org/10.1145/3147213.3147228openalex-snapshot
Locality-Aware Load Sharing in Mobile Cloud Computing
inferred← citeshttps://doi.org/10.1145/3197231.3197256openalex-snapshot
Code offloading solutions for audio processing in mobile healthcare applications
inferred← citeshttps://doi.org/10.1145/3204949.3204975openalex-snapshot
Latency and throughput characterization of convolutional neural networks for mobile computer vision
inferred← citeshttps://doi.org/10.1145/3439602.3439618openalex-snapshot
Harnessing the Computing Continuum for Urgent Science
inferred← citeshttps://doi.org/10.1145/3469029openalex-snapshot
Machine Learning at the Network Edge: A Survey
inferred← citeshttps://doi.org/10.1145/3486674openalex-snapshot
Edge Intelligence: Concepts, Architectures, Applications, and Future Directions
inferred← citeshttps://doi.org/10.1145/3744339openalex-snapshot
Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
inferred← citeshttps://doi.org/10.1186/s40537-020-00303-yopenalex-snapshot
Extending reference architecture of big data systems towards machine learning in edge computing environments
inferred← citeshttps://doi.org/10.13052/jmm1550-4646.1916openalex-snapshot
Analysis of Edge Intelligent Frameworks and their Security Issues
inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11854openalex-snapshot
r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches
inferred← citeshttps://doi.org/10.32604/cmes.2023.023684openalex-snapshot
Edge Intelligence with Distributed Processing of DNNs: A Survey
inferred← citeshttps://doi.org/10.3390/app9194193openalex-snapshot
SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data
inferred← citeshttps://doi.org/10.48550/arxiv.1711.07580openalex-snapshot
Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device
inferred← citeshttps://doi.org/10.48550/arxiv.2003.12172openalex-snapshot
Edge Intelligence: Architectures, Challenges, and Applications
inferred← citeshttps://doi.org/10.48550/arxiv.2006.04950openalex-snapshot
Machine Learning Systems for Intelligent Services in the IoT: A Survey
inferred← citeshttps://doi.org/10.48550/arxiv.2101.06171openalex-snapshot
Probabilistic Inference for Learning from Untrusted Sources
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.