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Depressive semantic awareness from vlog facial and vocal streams via spatio-temporal transformer

Digital Communications and Networks 2024年第3期10卷 577-585页

作者： Yongfeng Tao Minqiang Yang Yushan Wu Kevin Lee Adrienne Kline Bin Hu School of Information Science and Engineering Lanzhou UniversityLanzhouChina The School of Accounting Auditing and TaxationBusiness SchoolUNSW SydneyAustralia Department of Preventive Medicine Northwestern UniversityChicagoILUnited States

With the rapid growth of information transmission via the Internet,efforts have been made to reduce network load to promote *** such application is semantic computing,which can extract and process semantic *** media has enabled users to share their current emotions,opinions,and life events through their mobile ***,people suffering from mental health problems are more willing to share their feelings on social ***,it is necessary to extract semantic information from social media(vlog data)to identify abnormal emotional states to facilitate early identification and *** studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as *** solve this problem,this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of ***,a module with spatio-temporal data is embedded into the transformer encoder,which is utilized to obtain a representation of spatio-temporal ***,a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression *** are conducted on the D-Vlog *** results show that the method is effective,and the accuracy rate can reach 70.70%.This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.

关键词： Emotional computing semantic awareness Depression recognition Vlog data

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SuperFusion: A Versatile Image Registration and Fusion Network with semantic awareness

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2121-2137页

作者： Linfeng Tang Yuxin Deng Yong Ma Jun Huang Jiayi Ma Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision *** study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art *** source code and pre-trained model are publicly available at https://***/Linfeng-Tang/Super Fusion.

关键词： Global spatial attention image fusion image registration mutual promotion semantic awareness

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