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ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10578-9_23openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46478-7_31openalex-snapshot
A Discriminative Feature Learning Approach for Deep Face Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46487-9_50openalex-snapshot
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_32openalex-snapshot
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.fss.2008.11.017openalex-snapshot
A fuzzy approach to the evaluation of image complexity
inferredcites →https://doi.org/10.1016/j.imavis.2009.11.014openalex-snapshot
A survey on vision-based human action recognition
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cloud49709.2020.00088openalex-snapshot
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads
inferredcites →https://doi.org/10.1109/codesisss.2015.7331375openalex-snapshot
Big/little deep neural network for ultra low power inference
inferredcites →https://doi.org/10.1109/comsnets.2018.8328189openalex-snapshot
Minerva: A reinforcement learning-based technique for optimal scheduling and bottleneck detection in distributed factory operations
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2014.220openalex-snapshot
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-snapshot
FaceNet: A unified embedding for face recognition and clustering
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-snapshot
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
inferredcites →https://doi.org/10.1109/cvpr.2018.00919openalex-snapshot
BlockDrop: Dynamic Inference Paths in Residual Networks
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/ism.2020.00042openalex-snapshot
Closing-the-Loop: A Data-Driven Framework for Effective Video Summarization
inferredcites →https://doi.org/10.1109/jiot.2020.3007690openalex-snapshot
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
inferredcites →https://doi.org/10.1109/jssc.2016.2616357openalex-snapshot
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/micro.2014.53openalex-snapshot
SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers
inferredcites →https://doi.org/10.1109/micro.2014.62openalex-snapshot
Managing GPU Concurrency in Heterogeneous Architectures
inferredcites →https://doi.org/10.1109/nafips.2005.1548541openalex-snapshot
Image Complexity Measure: a Human Criterion Free Approach
inferredcites →https://doi.org/10.1109/qomex.2013.6603194openalex-snapshot
Image complexity and spatial information
inferredcites →https://doi.org/10.1109/tcsvt.2017.2736553openalex-snapshot
T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1145/1921621.1921624openalex-snapshot
Efficient incremental code update for sensor networks
inferredcites →https://doi.org/10.1145/2808719.2808761openalex-snapshot
An ensemble SVM model for the accurate prediction of non-canonical MicroRNA targets
inferredcites →https://doi.org/10.1145/2908080.2908087openalex-snapshot
Input responsiveness: using canary inputs to dynamically steer approximation
inferredcites →https://doi.org/10.1145/2980983.2908087openalex-snapshot
Input responsiveness: using canary inputs to dynamically steer approximation
inferredcites →https://doi.org/10.1145/2994551.2994564openalex-snapshot
Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables
inferredcites →https://doi.org/10.1145/3123266.3123389openalex-snapshot
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
inferredcites →https://doi.org/10.1145/3300061.3300116openalex-snapshot
Edge Assisted Real-time Object Detection for Mobile Augmented Reality
inferredcites →https://doi.org/10.3850/9783981537079_0819openalex-snapshot
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
inferredcites →https://doi.org/10.48550/arxiv.1406.2199openalex-snapshot
Two-Stream Convolutional Networks for Action Recognition in Videos
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.1504.04788openalex-snapshot
Compressing Neural Networks with the Hashing Trick
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1606.06160openalex-snapshot
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
inferredcites →https://doi.org/10.48550/arxiv.1609.07061openalex-snapshot
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
inferredcites →https://doi.org/10.48550/arxiv.1703.09844openalex-snapshot
Multi-Scale Dense Networks for Resource Efficient Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10844-025-00919-7openalex-snapshot
Integrating control-theoretic predictive deep learning for resource-efficient computing systems
inferred← citeshttps://doi.org/10.1109/access.2024.3520807openalex-snapshot
Real-Time Context-Aware Early Filtering for High-Definition Video Analytics on Commodity Edge Devices Using GenAI for Data Augmentation
inferred← citeshttps://doi.org/10.1109/comsnets63942.2025.10885676openalex-snapshot
Adapt3D: Lightweight and Adaptive 3D Detection Framework for Mobile GPUs
inferred← citeshttps://doi.org/10.1109/comst.2023.3323091openalex-snapshot
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques
inferred← citeshttps://doi.org/10.1109/icdcs60910.2024.00066openalex-snapshot
Tangram: High-Resolution Video Analytics on Serverless Platform with SLO-Aware Batching
inferred← citeshttps://doi.org/10.1109/icpads60453.2023.00318openalex-snapshot
An Adaptive Teacher-Student Framework for Real-time Video Inference in Multi-User Heterogeneous MEC Networks
inferred← citeshttps://doi.org/10.1109/infocom48880.2022.9796984openalex-snapshot
FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics
inferred← citeshttps://doi.org/10.1109/jiot.2025.3554167openalex-snapshot
BlockHybrid: Accelerating Object Detection Pipelines With Hybrid Block-Wise Execution
inferred← citeshttps://doi.org/10.1109/ojcs.2021.3085846openalex-snapshot
Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale
inferred← citeshttps://doi.org/10.1109/percomworkshops56833.2023.10150230openalex-snapshot
Content-Aware Dynamic Resource Allocation Framework for Video Analytic Applications at Edge
inferred← citeshttps://doi.org/10.1109/tccn.2025.3641511openalex-snapshot
Wider Eyes in the Sky: Scene-Adaptive Video Analytics in Low-Altitude Surveillance Networks
inferred← citeshttps://doi.org/10.1109/tmc.2025.3526573openalex-snapshot
FastTuner: Fast Resolution and Model Tuning for Multi-Object Tracking in Edge Video Analytics
inferred← citeshttps://doi.org/10.1109/wccct69960.2026.11549816openalex-snapshot
RECVA: Real-time Edge-assisted Cross-camera Video Analytics via Spatial-Temporal Redundancy Reduction
inferred← citeshttps://doi.org/10.1145/3469116.3470010openalex-snapshot
Benchmarking Video Object Detection Systems on Embedded Devices under Resource Contention
inferred← citeshttps://doi.org/10.1145/3510835openalex-snapshot
DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference
inferred← citeshttps://doi.org/10.1145/3564289openalex-snapshot
Virtuoso : Energy- and Latency-aware Streamlining of Streaming Videos on Systems-on-Chips
inferred← citeshttps://doi.org/10.1145/3582016.3582043openalex-snapshot
Kodan: Addressing the Computational Bottleneck in Space
inferred← citeshttps://doi.org/10.1145/3701562openalex-snapshot
Latency Reduction in Immersive Systems through Request Scheduling with Digital Twin Networks in Collaborative Edge Computing
inferred← citeshttps://doi.org/10.1145/3715014.3722044openalex-snapshot
Responsive DNN Adaptation for Video Analytics against Environment Shift via Hierarchical Mobile-Cloud Collaborations
inferred← citeshttps://doi.org/10.1145/3800227.3800238openalex-snapshot
Deep Temporal Convolutional Neural Networks with Attention Mechanisms for Resource Contention Classification in Cloud Computing
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