EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
暂无分享,去创建一个
Liang Wang | Xiaoyang Qu | Jianzong Wang | Jing Xiao | Guokuan Li | Nan Zhang | Ji-guang Wan | Guilin Jiang | Kaiyu Hu
[1] Xiaoyang Qu,et al. FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer , 2023, IJCAI.
[2] Liang Wang,et al. Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning , 2023, ArXiv.
[3] Nanqing Dong,et al. Edge Computing with Artificial Intelligence: A Machine Learning Perspective , 2022, ACM Comput. Surv..
[4] Katarina Grolinger,et al. Edge-Cloud Computing for Internet of Things Data Analytics: Embedding Intelligence in the Edge With Deep Learning , 2021, IEEE Transactions on Industrial Informatics.
[5] Paramvir Bahl,et al. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers , 2020, NSDI.
[6] Xiaoyang Qu,et al. Quantization and Knowledge Distillation for Efficient Federated Learning on Edge Devices , 2020, 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).
[7] Hui Zhang,et al. An Efficient Joint Training Framework for Robust Small-Footprint Keyword Spotting , 2020, ICONIP.
[8] H. Bal,et al. Clownfish: Edge and Cloud Symbiosis for Video Stream Analytics , 2020, 2020 IEEE/ACM Symposium on Edge Computing (SEC).
[9] Mehrdad Khani Shirkoohi,et al. Real-Time Video Inference on Edge Devices via Adaptive Model Streaming , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Masashi Sugiyama,et al. Rethinking Importance Weighting for Deep Learning under Distribution Shift , 2020, NeurIPS.
[11] Hong-Yuan Mark Liao,et al. YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.
[12] Mohamed R. Ibrahim,et al. WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning , 2019, ISPRS Int. J. Geo Inf..
[13] Yizhou Wang,et al. Multi-Level Domain Adaptive Learning for Cross-Domain Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[14] F. Richard Yu,et al. Power-Constrained Edge Computing With Maximum Processing Capacity for IoT Networks , 2019, IEEE Internet of Things Journal.
[15] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[16] Ion Stoica,et al. Chameleon: scalable adaptation of video analytics , 2018, SIGCOMM.
[17] Alexander J. Smola,et al. Detecting and Correcting for Label Shift with Black Box Predictors , 2018, ICML.
[18] Paramvir Bahl,et al. Focus: Querying Large Video Datasets with Low Latency and Low Cost , 2018, OSDI.
[19] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[21] Paramvir Bahl,et al. Live Video Analytics at Scale with Approximation and Delay-Tolerance , 2017, NSDI.
[22] Weisong Shi,et al. The Promise of Edge Computing , 2016, Computer.
[23] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Ming-Hsuan Yang,et al. UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking , 2015, Comput. Vis. Image Underst..
[25] Stephen J. Wright. Coordinate descent algorithms , 2015, Mathematical Programming.
[26] Yurii Nesterov,et al. Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..
[27] L. Breiman. Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.
[28] Kevin Hsieh,et al. RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics , 2023, NSDI.
[29] Feng-Feng Wei,et al. Combining Traffic Assignment and Traffic Signal Control for Online Traffic Flow Optimization , 2022, ICONIP.
[30] Shradha Verma,et al. RVFL Classifier Based Ensemble Deep Learning for Early Diagnosis of Alzheimer's Disease , 2022, ICONIP.
[31] Zhigang Luo,et al. Counterfactual Causal Adversarial Networks for Domain Adaptation , 2022, ICONIP.
[32] Francisco Herrera,et al. A unifying view on dataset shift in classification , 2012, Pattern Recognit..