Search Results for author: Miao Sun

Found 9 papers, 3 papers with code

LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection

1 code implementation29 Jan 2024 Sifan Zhou, Liang Li, Xinyu Zhang, Bo Zhang, Shipeng Bai, Miao Sun, Ziyu Zhao, Xiaobo Lu, Xiangxiang Chu

To our knowledge, for the very first time in lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the same as the FP32 model while enjoying $3\times$ inference speedup.

3D Object Detection Autonomous Vehicles +3

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

1 code implementation10 Nov 2017 Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, Ge Chen

This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS).

General Classification Oceanic Eddy Classification

ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network

no code implementations19 Oct 2017 Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun, Haiqing Jiang, Zhangxin Chen

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques.

Machine Translation Protein Function Prediction +1

Age Group and Gender Estimation in the Wild with Deep RoR Architecture

no code implementations9 Oct 2017 Ke Zhang, Ce Gao, Liru Guo, Miao Sun, Xingfang Yuan, Tony X. Han, Zhenbing Zhao, Baogang Li

In this paper, we propose a new CNN based method for age group and gender estimation leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group and gender classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. In order to further improve the performance and alleviate over-fitting problem, RoR model is pre-trained on ImageNet firstly, and then it is fune-tuned on the IMDB-WIKI-101 data set for further learning the features of face images, finally, it is used to fine-tune on Adience data set.

Ranked #5 on Age And Gender Classification on Adience Age (using extra training data)

Age And Gender Classification Age and Gender Estimation +1

Multiple Instance Learning Convolutional Neural Networks for Object Recognition

no code implementations11 Oct 2016 Miao Sun, Tony X. Han, Ming-Chang Liu, Ahmad Khodayari-Rostamabad

In this paper, we propose a weakly supervised CNN framework named Multiple Instance Learning Convolutional Neural Networks (MILCNN) to solve this problem.

Data Augmentation Multiple Instance Learning +4

Residual Networks of Residual Networks: Multilevel Residual Networks

1 code implementation9 Aug 2016 Ke Zhang, Miao Sun, Tony X. Han, Xingfang Yuan, Liru Guo, Tao Liu

This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks.

Image Classification

Latent Model Ensemble with Auto-localization

no code implementations15 Apr 2016 Miao Sun, Tony X. Han, Xun Xu, Ming-Chang Liu, Ahmad Khodayari-Rostamabad

Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene label- ing, due to their large learning capacity and resistance to overfit.

Classification General Classification +3

A Classification Leveraged Object Detector

no code implementations7 Apr 2016 Miao Sun, Tony X. Han, Zhihai He

Currently, the state-of-the-art image classification algorithms outperform the best available object detector by a big margin in terms of average precision.

Classification General Classification +4

Generic Object Detection With Dense Neural Patterns and Regionlets

no code implementations16 Apr 2014 Will Y. Zou, Xiaoyu Wang, Miao Sun, Yuanqing Lin

This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection.

Object object-detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.