136 citations · 455 across the 9 of their papers we have counts for
9 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
pBeeGees: A Prudent Approach to Certificate-Decoupled BFT Consensus
Kaiji Yang, Jingjing Zhang, Junyao Zheng +3
Pipelined Byzantine Fault Tolerant (BFT) consensus is fundamental to permissioned blockchains. However, many existing protocols are limited by the requirement for view-consecutive…
PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
Juncai Peng, Yi Liu, Shiyu Tang +13
Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of…
PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
Guanghua Yu, Qinyao Chang, Wenyu Lv +12
The better accuracy and efficiency trade-off has been a challenging problem in object detection. In this work, we are dedicated to studying key optimizations and neural network arc…
PP-ShiTu: A Practical Lightweight Image Recognition System
Shengyu Wei, Ruoyu Guo, Cheng Cui +10
In recent years, image recognition applications have developed rapidly. A large number of studies and techniques have emerged in different fields, such as face recognition, pedestr…
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
Yuning Du, Chenxia Li, Ruoyu Guo +9
Optical Character Recognition (OCR) systems have been widely used in various of application scenarios. Designing an OCR system is still a challenging task. In previous work, we pro…
PP-LCNet: A Lightweight CPU Convolutional Neural Network
Cheng Cui, Tingquan Gao, Shengyu Wei +10
We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks. This paper l…
PP-YOLOv2: A Practical Object Detector
Xin Huang, Xinxin Wang, Wenyu Lv +10
Being effective and efficient is essential to an object detector for practical use. To meet these two concerns, we comprehensively evaluate a collection of existing refinements to…
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Cheng Cui, Ruoyu Guo, Yuning Du +10
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance. Self-supervision and semi-supervised learning…