76 citations · 154 across the 9 of their papers we have counts for
9 papers
Modeling Global Distribution for Federated Learning with Label Distribution Skew
Tao Sheng, Chengchao Shen, Yuan Liu +3
Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more…
Bidirectional Regression for Arbitrary-Shaped Text Detection
Tao Sheng, Zhouhui Lian
Arbitrary-shaped text detection has recently attracted increasing interests and witnessed rapid development with the popularity of deep learning algorithms. Nevertheless, existing…
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
Tao Sheng, Jie Chen, Zhouhui Lian
Scene text detection remains a grand challenge due to the variation in text curvatures, orientations, and aspect ratios. One of the hardest problems in this task is how to represen…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
Chen Feng, Tao Sheng, Zhiyu Liang +9
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems wi…
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Qijie Zhao, Tao Sheng, Yongtao Wang +4
Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask R-CN…
2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton +38
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technolog…
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
Qijie Zhao, Tao Sheng, Yongtao Wang +2
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective ye…
A Quantization-Friendly Separable Convolution for MobileNets
Tao Sheng, Chen Feng, Shaojie Zhuo +3
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as netwo…