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Mutual Information Regularized Feature-Level Frankenstein for Discriminative Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Deep learning recognition approaches can potentially perform better if we can extract a discriminative representation that controllably separates nuisance factors.
Xiaofeng Liu +4 more
semanticscholar +1 more source
Mutual Support of Data Modalities in the Task of Sign Language Recognition
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021This paper presents a method for automatic sign language recognition that was utilized in the CVPR 2021 ChaLearn Challenge (RGB track). Our method is composed of several approaches combined in an ensemble scheme to perform isolated sign-gesture ...
Ivan Gruber +4 more
semanticscholar +1 more source
Heterogeneous Mutual Knowledge Distillation for Wearable Human Activity Recognition
IEEE Transactions on Neural Networks and Learning SystemsRecently, numerous deep learning algorithms have addressed wearable human activity recognition (HAR), but they often struggle with efficient knowledge transfer to lightweight models for mobile devices.
Zhiwen Xiao +7 more
semanticscholar +1 more source
Agribusiness
Geographical indication (GI) represents the specific good quality and reputation of the regional characteristics of agricultural products, which is a positive approach for stabilizing the export of agricultural products under China's new “Dual ...
Weiwen Qian, Yinguo Dong, Yuchen Liu
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Geographical indication (GI) represents the specific good quality and reputation of the regional characteristics of agricultural products, which is a positive approach for stabilizing the export of agricultural products under China's new “Dual ...
Weiwen Qian, Yinguo Dong, Yuchen Liu
semanticscholar +1 more source
Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition
International Conference on Medical Image Computing and Computer-Assisted InterventionAn objective and accurate emotion diagnostic reference is vital to psychologists, especially when dealing with patients who are difficult to communicate with for pathological reasons.
Yiming Zhao, Jin Gu
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Embedding Mutual Recognition at the WTO
SSRN Electronic Journal, 2006Abstract Mutual recognition is a useful tool for international liberalization in particular contexts. However, it poses two important types of risk. First, it could jeopardize a satisfactory level of prudential regulation. In order to address these risks, mutual recognition should be limited to initiatives that can be supported by satisfactory ...
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Between the medium and the minimum options to regulate mutual recognition of confiscation orders
New Journal of European Criminal Law, 2018The proposal for a regulation on the mutual recognition of freezing and confiscation orders is aimed at solving the problems of criminal asset recovery in cross-border cases.
Ariadna H. Ochnio
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How do mutual recognition agreements influence trade?
Review of Development Economics, 2018This paper empirically tests how the magnitude of trade promotion effects of mutual recognition agreements (MRAs) varies with various mediums. The main rationale for the research is that because an MRA eliminates technical barriers to trade (TBT), the ...
Yong Joon Jang
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Mutual Component Convolutional Neural Networks for Heterogeneous Face Recognition
IEEE Transactions on Image Processing, 2019Heterogeneous face recognition (HFR) aims to identify a person from different facial modalities, such as visible and near-infrared images. The main challenges of HFR lie in the large modality discrepancy and insufficient training samples.
Zhongying Deng +3 more
semanticscholar +1 more source
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2019
A difficulty in human activity recognition (HAR) with wearable sensors is the acquisition of large amounts of annotated data for training models using supervised learning approaches.
Rebecca Adaimi, Edison Thomaz
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A difficulty in human activity recognition (HAR) with wearable sensors is the acquisition of large amounts of annotated data for training models using supervised learning approaches.
Rebecca Adaimi, Edison Thomaz
semanticscholar +1 more source