activity
20172020
most citedDeep Reinforcement Learning: Framework, Applications, and Embedded Implementations

15 citations · 21 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2020

YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

Yuxuan Cai, Hongjia Li, Geng Yuan +5

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-a…

cs.CR2020

ESMFL: Efficient and Secure Models for Federated Learning

Sheng Lin, Chenghong Wang, Hongjia Li +3

Nowadays, Deep Neural Networks are widely applied to various domains. However, massive data collection required for deep neural network reveals the potential privacy issues and als…

cs.CV2020

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Xiaolong Ma, Wei Niu, Tianyun Zhang +8

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on vario…

eess.IV20192 cited

Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

Hongjia Li, Sheng Lin, Ning Liu +2

Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accur…

cs.CV2018

Image Dataset for Visual Objects Classification in 3D Printing

Hongjia Li, Xiaolong Ma, Aditya Singh Rathore +5

The rapid development in additive manufacturing (AM), also known as 3D printing, has brought about potential risk and security issues along with significant benefits. In order to e…

cs.CV2018

C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Zhe Li, Xiaolong Ma, Hongjia Li +5

Increasing malicious users have sought practices to leverage 3D printing technology to produce unlawful tools in criminal activities. Current regulations are inadequate to deal wit…