no code implementations • 12 Apr 2024 • Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu
In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection).
no code implementations • 6 Mar 2024 • Chuanyu Luo, Nuo Cheng, Ren Zhong, Haipeng Jiang, Wenyu Chen, Aoli Wang, Pu Li
With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth.
no code implementations • 25 Jul 2023 • Chuanyu Luo, Nuo Cheng, Sikun Ma, Jun Xiang, Xiaohan Li, Shengguang Lei, Pu Li
The pioneer work PointNet has been widely applied as a local feature descriptor, a fundamental component in deep learning models for 3D perception, to extract features of a point cloud.
no code implementations • 14 Jul 2023 • Chuanyu Luo, Nuo Cheng, Sikun Ma, Han Li, Xiaohan Li, Shengguang Lei, Pu Li
Large-scale LiDAR-based point cloud semantic segmentation is a critical task in autonomous driving perception.
1 code implementation • 2 Jun 2023 • Yun Chu, Pu Li, Yong Bai, Zhuhua Hu, Yongqing Chen, Jiafeng Lu
To address these issues, for the object detection network we introduce a three-stage model compression method: dynamic sparse training, group channel pruning, and spatial attention distilling.
1 code implementation • 5 Apr 2023 • Pu Li, Xiaobai Liu
To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches.
1 code implementation • 5 Apr 2023 • Pu Li, Marie Roch, Holger Klinck, Erica Fleishman, Douglas Gillespie, Eva-Marie Nosal, Yu Shiu, Xiaobai Liu
To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals.
1 code implementation • CVPR 2023 • Pu Li, Jianwei Guo, Xiaopeng Zhang, Dong-Ming Yan
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem.
1 code implementation • 26 Apr 2022 • Hongyi Yao, Pu Li, Jian Cao, Xiangcheng Liu, Chenying Xie, Bingzhang Wang
We are the first to propose the more constrained but hardware-friendly Power-of-Two quantization scheme for low-bit PTQ specially and prove that it can achieve nearly the same accuracy as SOTA PTQ method.
no code implementations • 30 Jan 2022 • Chuanyu Luo, Xiaohan Li, Nuo Cheng, Han Li, Shengguang Lei, Pu Li
The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification.
no code implementations • 2 Dec 2020 • Wenyu Sun, Jian Cao, Pengtao Xu, Xiangcheng Liu, Pu Li
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process.
no code implementations • 29 Nov 2020 • Pengtao Xu, Jian Cao, Fanhua Shang, Wenyu Sun, Pu Li
For layer pruning, we convert convolutional layers of network into ResConv with a layer scaling factor.
no code implementations • 14 Jun 2020 • Pu Li, Xiang-Yang Li, Xiang Long
It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data.
no code implementations • 6 May 2020 • Xiang-Yang Li, Guo Pu, Keyu Ming, Pu Li, Jie Wang, Yuxuan Wang
In the traditional text style transfer model, the text style is generally relied on by experts knowledge and hand-designed rules, but with the application of deep learning in the field of natural language processing, the text style transfer method based on deep learning Started to be heavily researched.
no code implementations • 4 May 2020 • Adonis Bogris, Charis Mesaritakis, Stavros Deligiannidis, Pu Li
We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities.