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Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence

Lang He, Chenguang Guo, Prayag Tiwari, Hari Mohan Pandey, Wei Dang
<span title="2021-04-08">2021</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tsqpdv4ebvctjlbuqzeazjgjaq" style="color: black;">International Journal of Intelligent Systems</a> </i> &nbsp;
K E Y W O R D S 3D-CNN, depression, industrial intelligent system, pattern recognition, vector of local aggregated descriptors  ...  In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP-NetVLAD, are integrated to learn the compact characteristic based on the feature maps.  ...  The attention mechanism is proposed to represent the salient patterns in many raw signals (e.g., audio, video, text, etc.).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/int.22426">doi:10.1002/int.22426</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3pruu3nbx5hcpjndgasxpentri">fatcat:3pruu3nbx5hcpjndgasxpentri</a> </span>
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Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis

Wheidima Carneiro de Meto, Eric Granger, Miguel Bordallo Lopez
<span title="">2020</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection.  ...  Typically, depression levels are recognized using 2D-Convolutional Neural Networks (CNNs) that are trained to extract static features from video frames, which impairs the capture of dynamic spatio-temporal  ...  In [10] , the authors proposed to integrate the facial features by using an attention mechanism to fuse features from a video.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp40776.2020.9054375">doi:10.1109/icassp40776.2020.9054375</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/MeloGL20.html">dblp:conf/icassp/MeloGL20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p354cmtl3nbu3czxatost6i4ja">fatcat:p354cmtl3nbu3czxatost6i4ja</a> </span>
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Neural Architecture Searching for Facial Attributes-based Depression Recognition [article]

Mingzhe Chen, Xi Xiao, Bin Zhang, Xinyu Liu, Runiu Lu
<span title="2022-01-24">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recent studies show that depression can be partially reflected from human facial attributes.  ...  In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly  ...  , recent advances in deep learning (e.g., 1D-CNN [16] , [17] , LSTM [20] , attention-based temporal CNN [21] , Causal CNN [22] , etc.) also have been applied to infer depression from facial attribute  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.09799v1">arXiv:2201.09799v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2wvusqt5gjdg7mjysckpcd5pj4">fatcat:2wvusqt5gjdg7mjysckpcd5pj4</a> </span>
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An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos [article]

Sicheng Zhao, Yunsheng Ma, Yang Gu, Jufeng Yang, Tengfei Xing, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
<span title="2020-02-12">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Emotion recognition in user-generated videos plays an important role in human-centered computing.  ...  Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions  ...  Visual-Audio Attention Network We propose a novel CNN architecture with spatial, channelwise, and temporal attention mechanisms for emotion recognition in user generated videos.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.00832v1">arXiv:2003.00832v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ckcbrackkzfeditpjdfnoj3wqq">fatcat:ckcbrackkzfeditpjdfnoj3wqq</a> </span>
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Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition [article]

Gnana Praveen R, Eric Granger, Patrick Cardinal
<span title="2021-11-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we focus on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos, where complex spatiotemporal relationships may be captured.  ...  The effectiveness of the proposed approach is validated experimentally on videos from the RECOLA and Fatigue (private) data-sets.  ...  CONCLUSION In this paper, we introduce cross-attentional A-V fusion for dimensional emotion recognition from videos.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.05222v1">arXiv:2111.05222v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/up6hfpvfx5h3vjb3qkajxotzya">fatcat:up6hfpvfx5h3vjb3qkajxotzya</a> </span>
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An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Sicheng Zhao, Yunsheng Ma, Yang Gu, Jufeng Yang, Tengfei Xing, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Emotion recognition in user-generated videos plays an important role in human-centered computing.  ...  Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions  ...  New Generation of AI Grant (No. 2018AAA010040003), Natural Science Foundation of Tianjin, China (Nos.18JCYBJC15400, 18ZXZNGX00110), and the Open Project Program of the National Laboratory of Pattern Recognition  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i01.5364">doi:10.1609/aaai.v34i01.5364</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/atlqqdxnwrhvhokkbubilxesla">fatcat:atlqqdxnwrhvhokkbubilxesla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106082444/https://www.aiide.org/ojs/index.php/AAAI/article/download/5364/5220" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/25/0b/250b8c5686aa00c9079cad164843a623ea206bae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i01.5364"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Analysis of student sentiment during video class with multi-layer deep learning approach

Imrus Salehin, Nazmun Nessa Moon, Iftakhar Mohammad Talha, Md. Mehedi Hasan, Farnaz Narin Nur Hasan, Md. Azizul Hakim, A S M Farhan Al Haque
<span title="2022-08-01">2022</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c3tlqqlmlrhohmjgwq5tzpde6m" style="color: black;">International Journal of Power Electronics and Drive Systems (IJPEDS)</a> </i> &nbsp;
For output measure, we divide the final output result as attentive, inattentive, understand, and neutral.  ...  Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work.  ...  CNN for single image facial expression target matching When we get images from the real-time video, we have used a face detection algorithm and also train by this image with a dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijece.v12i4.pp3981-3993">doi:10.11591/ijece.v12i4.pp3981-3993</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/55cgfwkf7jgixcigtszmioxlfm">fatcat:55cgfwkf7jgixcigtszmioxlfm</a> </span>
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Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition [article]

Dimitrios Kollias, Stefanos Zafeiriou
<span title="2019-12-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The developed deep neural architectures are based on the joint training of state-of-the-art convolutional and recurrent neural networks with attention mechanism; thus exploiting both the invariant properties  ...  The videos are downloaded from Youtube and have large variations in pose, age, illumination conditions, ethnicity and profession.  ...  The proposed deep architectures are composed of convolutional and recurrent neural network (CNN-RNN) layers, along with an attention mechanism.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.07770v2">arXiv:1811.07770v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/36bfuf3jmreobbktnoedsjcoxq">fatcat:36bfuf3jmreobbktnoedsjcoxq</a> </span>
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A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
<span title="2022-03-20">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, it is hard to reveal one's inner emotion hidden purposely from facial expressions, audio tones, body gestures, etc.  ...  ., emotion recognition and sentiment analysis).  ...  Specifically, spatial, channelwise, and temporal attentions are integrated with a 3D CNN [366] for video frame segments, and spatial attention is integrated with a 2D CNN (ResNet-18) for audio MFCC segments  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.06935v3">arXiv:2203.06935v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h4t3omkzjvcejn2kpvxns7n2qe">fatcat:h4t3omkzjvcejn2kpvxns7n2qe</a> </span>
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Development and application of animal behavior analysis system using video images

Naoaki SAKAMOTO, Yusuke MIYAZAKI, Koji KOBAYASHI, Takahisa MURATA
<span title="">2022</span> <i title="AMED iD3 Catalyst Unit"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zqid3u6ntnfxloepnxvqydjtie" style="color: black;">Translational and Regulatory Sciences</a> </i> &nbsp;
We developed a system to assess the behavior of laboratory animals using video analysis technologies and artificial intelligence.  ...  There is an urgent need for the development of therapeutic drugs for central nervous system (CNS) diseases, such as depression, dementia, and pain.  ...  Acknowledgements This work was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (19K15975 to Koji Kobayashi, 20H05678 and 17H06252 to Takahisa Murata  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33611/trs.2022-002">doi:10.33611/trs.2022-002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4lmryv5pabbhviwcal5zsaqcpe">fatcat:4lmryv5pabbhviwcal5zsaqcpe</a> </span>
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2020 Index IEEE Journal of Selected Topics in Signal Processing Vol. 14

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aznf273kcvcbfjcdeghr3xjd6i" style="color: black;">IEEE Journal on Selected Topics in Signal Processing</a> </i> &nbsp;
., +, JSTSP Aug. 2020 997-1011 Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders.  ...  Haider, F., +, JSTSP Feb. 2020 272-281 Automatic Assessment of Depression From Speech via a Hierarchical Atten- tion Transfer Network and Attention Autoencoders.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstsp.2020.3029672">doi:10.1109/jstsp.2020.3029672</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6twwzcqpwzg4ddcu2et75po77u">fatcat:6twwzcqpwzg4ddcu2et75po77u</a> </span>
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Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning

Cristina Luna-Jiménez, David Griol, Zoraida Callejas, Ricardo Kleinlein, Juan M. Montero, Fernando Fernández-Martínez
<span title="2021-11-18">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Regarding the facial emotion recognizers, we propose a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism  ...  The error analysis reported that the frame-based systems could present some problems when they were used directly to solve a video-based task despite the domain adaptation, which opens a new line of research  ...  Bidirectional-LSTM with attention mechanism for facial emotion recognition at the video level. Modified version from source [77]. Figure 5 . 5 Figure 5.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21227665">doi:10.3390/s21227665</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34833739">pmid:34833739</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8618559/">pmcid:PMC8618559</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/umfcwlktlbcq7cxtualceg7njy">fatcat:umfcwlktlbcq7cxtualceg7njy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211201090515/https://mdpi-res.com/d_attachment/sensors/sensors-21-07665/article_deploy/sensors-21-07665.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/32/3f/323f5d0af9de0610c5e25a84b1a67988833b3f8f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21227665"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618559" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks [article]

Wheidima Carneiro de Melo, Eric Granger, Miguel Bordallo Lopez
<span title="2022-03-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Video-based analysis of facial expressions has been increasingly applied to infer health states of individuals, such as depression and pain.  ...  Results also indicate that our DMSN architecture provides a cost-effective solution for expressions that range from fewer facial variations over time, as in depression detection, to greater variations,  ...  [32] used ResNet-50 to explore the appearance information, and attention mechanism to fuse the static facial features.  ... 
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Aff-Wild Database and AffWildNet [article]

Mengyao Liu, Dimitrios Kollias
<span title="2019-12-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination.  ...  By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism.  ...  VGGFace network pre-trained on VGGFace database is used as CNN block and 2 layers GRU having 128 hidden units with attention mechanism having attention length 30 is applied as RNN block.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.05318v2">arXiv:1910.05318v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4txkyyztsvcxhn52errqxqeeyq">fatcat:4txkyyztsvcxhn52errqxqeeyq</a> </span>
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Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition [article]

Siyang Song, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, Hatice Gunes
<span title="2021-10-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Consequently, we propose to represent the target subjects (defined as the listener) person-specific cognition in the form of a person-specific CNN architecture that has unique architectural parameters  ...  Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function, which is then represented as a graph representation for recognising the target subject's  ...  In some scenarios, the goal is to infer true personality from machine detectable distal cues, i.e., Automatic Personality Recognition (APR) [91] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.13570v2">arXiv:2110.13570v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y7qphshux5f27dwg3uqqadk2pi">fatcat:y7qphshux5f27dwg3uqqadk2pi</a> </span>
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