Search Results for author: Jing Han

Found 21 papers, 2 papers with code

Dual camera snapshot hyperspectral imaging system via physics informed learning

no code implementations6 Sep 2021 Hui Xie, Zhuang Zhao, Jing Han, Yi Zhang, Lianfa Bai, Jun Lu

Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.

Segmentation-free Heart Pathology Detection Using Deep Learning

no code implementations9 Aug 2021 Erika Bondareva, Jing Han, William Bradlow, Cecilia Mascolo

Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination.

Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data

no code implementations5 Apr 2021 Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo

To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

no code implementations24 Feb 2021 Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.

Unsupervised Representation Learning

Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation

no code implementations28 Dec 2020 Xiaoyu Chen, Chi Zhang, Guosheng Lin, Jing Han

Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.

Few-Shot Learning Point Cloud Segmentation +1

LiDAR Data Enrichment Using Deep Learning Based on High-Resolution Image: An Approach to Achieve High-Performance LiDAR SLAM Using Low-cost LiDAR

no code implementations9 Aug 2020 Jiang Yue, Weisong Wen, Jing Han, Li-Ta Hsu

Inspired by the complementariness of 3D LiDAR and camera, this paper proposes to make use of the high-resolution images from a camera to enrich the raw 3D point clouds from the low-cost 16 channels LiDAR based on a state-of-the-art deep learning algorithm.

Autonomous Driving

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

3 code implementations10 Jun 2020 Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Cecilia Mascolo

This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.

COVID-19 Diagnosis

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

no code implementations30 Apr 2020 Jing Han, Kun Qian, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu, Huaiyuan Zheng, Wei Ji, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, Björn W. Schuller

In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.

Sleep Quality

Learning-based real-time method to looking through scattering medium beyond the memory effect

no code implementations20 Oct 2019 Enlai Guo, Shuo Zhu, Yan Sun, Lianfa Bai, Jing Han

Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields.

EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings

no code implementations23 Jul 2019 Jing Han, Zixing Zhang, Zhao Ren, Björn Schuller

Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand.

Emotion Classification Emotion Recognition

High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing

no code implementations29 Jun 2019 XiaoYu Chen, Xu Wang, Lianfa Bai, Jing Han, Zhuang Zhao

In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time.

Non-destructive three-dimensional measurement of hand vein based on self-supervised network

no code implementations29 Jun 2019 Xiaoyu Chen, Qixin Wang, Jinzhou Ge, Yi Zhang, Jing Han

At present, supervised stereo methods based on deep neural network have achieved impressive results.

Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

no code implementations29 Mar 2019 Zixing Zhang, Jing Han, Kun Qian, Christoph Janott, Yanan Guo, Bjoern Schuller

One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data.

Classification Data Augmentation +1

Residual Pyramid Learning for Single-Shot Semantic Segmentation

1 code implementation23 Mar 2019 Xiaoyu Chen, Xiaotian Lou, Lianfa Bai, Jing Han

In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks.

Semantic Segmentation

SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild

no code implementations9 Jan 2019 Jean Kossaifi, Robert Walecki, Yannis Panagakis, Jie Shen, Maximilian Schmitt, Fabien Ringeval, Jing Han, Vedhas Pandit, Antoine Toisoul, Bjorn Schuller, Kam Star, Elnar Hajiyev, Maja Pantic

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.

Scaling Speech Enhancement in Unseen Environments with Noise Embeddings

no code implementations26 Oct 2018 Gil Keren, Jing Han, Björn Schuller

We address the problem of speech enhancement generalisation to unseen environments by performing two manipulations.

Speech Enhancement Speech Recognition

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

no code implementations21 Sep 2018 Jing Han, Zixing Zhang, NIcholas Cummins, Björn Schuller

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.

Sentiment Analysis

Learning audio sequence representations for acoustic event classification

no code implementations27 Jul 2017 Zixing Zhang, Ding Liu, Jing Han, Kun Qian, Björn Schuller

Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.

Classification General Classification

Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network

no code implementations12 May 2017 Yahui Liu, Jian Yao, Li Li, Xiaohu Lu, Jing Han

We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.

BSDS500 Contour Detection

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