1 code implementation • 19 Sep 2024 • Jingyuan Wang, Jie Zhang, Shihao Chen, Miao Sun
Binaural speech enhancement (BSE) aims to jointly improve the speech quality and intelligibility of noisy signals received by hearing devices and preserve the spatial cues of the target for natural listening.
1 code implementation • 12 Sep 2024 • Jian Qian, Miao Sun, Ashley Lee, Jie Li, Shenglong Zhuo, Patrick Yin Chiang
The network consists of an input module for the depth map and RGB image features extraction and concatenation, a U-shaped encoder-decoder Transformer for extracting deep features, and a refinement module.
1 code implementation • 8 Jul 2024 • Jian Qian, Miao Sun, Sifan Zhou, Ziyu Zhao, Ruizhi Hun, Patrick Chiang
In Sub-SA, we design a submodular function that facilitates effective subset selection for annotation and demonstrates the characteristics of monotonically and submodularity from the theoretical perspective.
no code implementations • 5 Jul 2024 • Jian Qian, Bingyu Xie, Biao Wan, Minhao Li, Miao Sun, Patrick Yin Chiang
TimeLDM is composed of a variational autoencoder that encodes time series into an informative and smoothed latent content and a latent diffusion model operating in the latent space to generate latent information.
1 code implementation • 29 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.
1 code implementation • 10 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).
no code implementations • 19 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.
no code implementations • 9 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 #6 on
Age And Gender Classification
on Adience Age
(using extra training data)
no code implementations • 11 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.
1 code implementation • 9 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.
Ranked #15 on
Image Classification
on SVHN
no code implementations • 15 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.
no code implementations • 7 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.
no code implementations • 16 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.