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7th International Conference on network analysis, NET 2017

作者： Koldanov, Petr A. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Bolshaya Pecherskaya 25/12 Nizhny Novgorod603155 Russia

Invariance properties of statistical procedures for threshold graph identification are considered. An optimal procedure in the class of invariant multiple decision procedures is constructed. © Springer Internatio... 详细信息

ISBN: (纸本)9783319962467

关键词： Random variables

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CA-SER: Cross-Attention Feature Fusion for Speech Emotion Recognition 27

CA-SER: Cross-Attention Feature Fusion for Speech Emotion Re...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Deeb, Bashar M. Savchenko, Andrey Makarov, Ilya MIPT Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia Sber AI Lab Moscow Russia Moscow Russia ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia

ISBN: (纸本)9781643685489

In this paper, we introduce a novel tool for speech emotion recognition, CA-SER, that borrows self-supervised learning to extract semantic speech representations from a pre-trained wav2vec 2.0 model and combine them with spectral audio features to improve speech emotion recognition. Our approach involves a self-attention encoder on MFCC features to capture meaningful patterns in audio sequences. These MFCC features are combined with high-level representations using a multi-head cross-attention mechanism. Evaluation of speech emotion recognition on the IEMOCAP dataset shows that our system achieves a weighted accuracy of 74.6%, outperforming most existing techniques. © 2024 The Authors.

关键词： Self-supervised learning

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Compressing deep convolutional neural networks in visual emotion recognition

Compressing deep convolutional neural networks in visual emo...

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2017 International Conference Information Technology and Nanotechnology. Session Image Processing, Geoinformation Technology and Information Security, IPGTIS-ITNT 2017

作者： Rassadin, A.G. Savchenko, A.V. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis 25/12 Bolshaya Pecherskaya Street Nizhny Novgorod603155 Russia

In this paper, we consider the problem of insufficient runtime and memory-space complexities of deep convolutional neural networks for visual emotion recognition. A survey of recent compression methods and efficient neural networks architectures is provided. We experimentally compare the computational speed and memory consumption during the training and the inference stages of such methods as the weights matrix decomposition, binarization and hashing. It is shown that the most efficient optimization can be achieved with the matrices decomposition and hashing. Finally, we explore the possibility to distill the knowledge from the large neural network, if only large unlabeled sample of facial images is available.

关键词： Emotion Recognition

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hsemotion Team at the 6th ABAW Competition: Facial Expressions, Valence-Arousal and Emotion Intensity Prediction

arXiv

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arXiv 2024年

作者： Savchenko, Andrey V. Sber AI Lab Moscow Russia HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

This article presents our results for the sixth Affective Behavior analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, action units, facial expressions, and valence/arousal. Experimental results for five tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble *** Codes 68T10 Copyright © 2024, The Authors. All rights reserved.

关键词： Feedforward neural networks

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Improved Credit Scoring Model with Hyperparameter Optimization 23rd

Improved Credit Scoring Model with Hyperparameter Optimizati...

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23rd International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Marciano, Chiara Guarracino, Mario Rosario Bernhardt, Brian Daniel University of Cassino and Southern Lazio Cassino Italy Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics - Nizhny Novgorod Nizhny Novgorod Russia

ISBN: (纸本)9783031733642

This article investigates the application of machine learning techniques for predicting corporate default risk. In the credit scoring domain, the class imbalance problem is prevalent, with defaulted cases typically being much less numerous than non-defaulted ones. To address challenges posed by unbalanced datasets, various calibration methodologies, including data sampling strategies and Synthetic Minority Oversampling Technique (SMOTE), are explored to identify the most effective strategy for the specific case. Additionally, hyperparameter tuning plays a crucial role in enhancing machine learning model performance. Therefore, we employ hyperparameter optimization techniques such as Grid Search, Random Search, Bayesian Optimization, and Adaptive Particle Swarm Optimization. The primary objective is to compare these techniques and determine the optimal strategy for predicting company default events. Our evaluation incorporates standard parameters such as accuracy, balanced accuracy, specificity, sensitivity, and F1 score. Another factor to consider to discriminate the various approaches is the execution time, to be able to develop models that can be implemented in market products. The experimental results reveal which approaches result in significantly improved classification performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Particle swarm optimization (PSO)

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hsemotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognition

arXiv

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arXiv 2024年

作者： Savchenko, Andrey V. Sber AI Lab Moscow Russia HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this paper, we describe the results of the hsemotion team in two tasks of the seventh Affective Behavior analysis in-the-wild (ABAW) competition, namely, multi-task learning for simultaneous prediction of facial expression, valence, arousal, and detection of action units, and compound expression recognition. We propose an efficient pipeline based on frame-level facial feature extractors pre-trained in multi-task settings to estimate valence-arousal and basic facial expressions given a facial photo. We ensure the privacy-awareness of our techniques by using the lightweight architectures of neural networks, such as MT-EmotiDDAMFN, MT-EmotiEffNet, and MT-EmotiMobileFaceNet, that can run even on a mobile device without the need to send facial video to a remote server. It was demonstrated that a significant step in improving the overall accuracy is the smoothing of neural network output scores using Gaussian or box filters. It was experimentally demonstrated that such a simple post-processing of predictions from simple blending of two top visual models improves the F1-score of facial expression recognition up to 7%. At the same time, the mean Concordance Correlation Coefficient (CCC) of valence and arousal is increased by up to 1.25 times compared to each model’s frame-level predictions. As a result, our final performance score on the validation set from the multi-task learning challenge is 4.5 times higher than the baseline (1.494 vs 0.32).MSC Codes 68T10 Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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Leveraging Pre-trained Multi-task Deep Models for Trustworthy Facial analysis in Affective Behaviour analysis in-the-Wild

Leveraging Pre-trained Multi-task Deep Models for Trustworth...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Andrey V. Savchenko Sber AI Lab Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This article presents our results for the sixth Affective Behavior analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, and valence/arousal. Experimental results for three tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our solutions took second place in the compound expression recognition competition.

关键词： Training Analytical models Correlation Electromagnetic interference Estimation Predictive models Feature extraction

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EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition

arXiv

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arXiv 2023年

作者： Savchenko, Andrey V. Sber AI Lab Moscow Russia HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this article, the results of our team for the fifth Affective Behavior analysis in-the-wild (ABAW) competition are presented. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model achieves a much greater macro-averaged F1-score for facial expression recognition and action unit detection and concordance correlation coefficients for valence/arousal estimation when compared to *** Codes 68T10 Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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Learning Facial Expression Recognition In-the-Wild from Synthetic Data Based on an Ensemble of Lightweight Neural networks 11th

Learning Facial Expression Recognition In-the-Wild from Syn...

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11th International Conference on analysis of Images, Social networks and Texts , AIST 2023

作者： Nguyen, Long Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia Sber AI Lab Moscow Russia

ISBN: (纸本)9783031670077

This paper deals with one of the problems of recognizing the emotion from a photo gathered from in-the-wild settings, namely, facial expression recognition. We study various ensemble approaches that combine the lightweight MT-EmotiEffNet, attention-based transformer, and the graph-based model. The models have been implemented on an edge device (Jetson Nano) to be integrated into a demo application for video analytics to evaluate their performance in practical conditions. The experimental results demonstrated that the proposed approach outperformed competitive solutions using the Learning from Synthetic Data challenge datasets of the fourth Affective Behavior analysis in-the-Wild competition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Graphic methods

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EmotiEffNets for Facial Processing in Video-based Valence-Arousal Prediction, Expression Classification and Action Unit Detection

EmotiEffNets for Facial Processing in Video-based Valence-Ar...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Andrey V. Savchenko Sber AI Lab Moscow Russia HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this article, the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction are used for downstream emotion analysis tasks from the fifth Affective Behavior analysis in-the-wild (ABAW) competition. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model is much better than the baseline facial processing using VGGFace And ResNet. For example, our macro-averaged F1-scores of facial expression recognition and action unit detection on the testing set are 11-13% greater. Moreover, the concordance correlation coefficients for valence/arousal estimation are up to 30% higher when compared to the baseline.

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