Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications

2014 ◽  
Author(s):  
Shuixian Chen ◽  
Xiang Lu ◽  
Limin Sun ◽  
Shiming Ge
Keyword(s):  
Face Detection ◽  
Domain Adaptation ◽  
Peer To Peer ◽  
Smart Camera ◽  
Camera Network ◽  
Target Domain

scholarly journals Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1579 ◽  
Author(s):  
Kyoung Ju Noh ◽  
Chi Yoon Jeong ◽  
Jiyoun Lim ◽  
Seungeun Chung ◽  
Gague Kim ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Classification Model ◽  
Speech Emotion Recognition ◽  
Target Domain ◽  
Model Generalization ◽  
Speech Database ◽  
Emotion Labels ◽  
Temporal Feature

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

scholarly journals Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

2021 ◽  
Vol 10 (8) ◽  
pp. 523
Author(s):  
Nicholus Mboga ◽  
Stefano D’Aronco ◽  
Tais Grippa ◽  
Charlotte Pelletier ◽  
Stefanos Georganos ◽  
...  
Keyword(s):  
Land Cover ◽  
Domain Adaptation ◽  
Central Africa ◽  
Semantic Segmentation ◽  
Target Domain ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
The Cost ◽  
Performance Gains

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved.

scholarly journals Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

2021 ◽  
Vol 11 (5) ◽  
pp. 603
Author(s):  
Chunlei Shi ◽  
Xianwei Xin ◽  
Jiacai Zhang
Keyword(s):  
Machine Learning ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Target Domain ◽  
Sample Distribution ◽  
Machine Learning Methods ◽  
First Time

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

Toward low latency gesture control using smart camera network

2008 ◽  
Author(s):  
Zoran Zivkovic ◽  
Vitaly Kliger ◽  
Richard Kleihorst ◽  
Alexander Danilin ◽  
Ben Schueler ◽  
...  
Keyword(s):  
Low Latency ◽  
Smart Camera ◽  
Camera Network ◽  
Gesture Control

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

2021 ◽  
pp. 095440622110329
Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Training Data ◽  
Variable Load ◽  
K Nearest Neighbor ◽  
Target Domain ◽  
Bearing Faults ◽  
Load Conditions

To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

2019 ◽  
Vol 33 (05) ◽  
pp. 1956005 ◽  
Author(s):  
Yongjie Chu ◽  
Yong Zhao ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Domain Adaptation ◽  
Face Image ◽  
Single Sample ◽  
Low Resolution ◽  
Target Domain ◽  
Discriminative Ability ◽  
Surveillance Camera ◽  
Single Sample Per Person ◽  
Suspect Identification

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

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