2 citations · 4 across the 3 of their papers we have counts for
7 papers
Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices
Tianyi Wang, Zichen Wang, Cong Wang +4
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorit…
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +3
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to…
Custom Object Detection via Multi-Camera Self-Supervised Learning
Yan Lu, Yuanchao Shu
This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks. MCSSL associates bounding boxes between cameras…
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
Mi Zhang, Faen Zhang, Nicholas D. Lane +5
The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extractin…
WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes
Zheng Dong, Yan Lu, Guangmo Tong +3
Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent…
ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
Samvit Jain, Xun Zhang, Yuhao Zhou +4
Enterprises are increasingly deploying large camera networks for video analytics. Many target applications entail a common problem template: searching for and tracking an object or…