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12th International Conference on analysis of Images, Social networks and Texts, AIST 2024

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

ISBN: (纸本)9783031880353

Emotion recognition from multimodal sources is essential for advancing human-computer interaction. This paper introduces a comprehensive approach integrating robust methodologies to enhance multimodal emotion recognition. By harnessing both facial video and audio signals through Temporal Convolutional networks (TCN) and Transformer models, alongside other advanced neural architectures, our ensemble effectively captures the complex dynamics of emotional expressions. Our approach has been rigorously evaluated on the Expression Classification challenge from the recent Affective Behavior analysis in-the-Wild competition, demonstrating significant improvements compared to state-of-the-art methods. Specifically, our model achieved a 12% increase in F1 Score over the baseline, illustrating a substantial enhancement in performance and reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition

arXiv

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arXiv 2025年

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

This article presents our results for the eighth Affective Behavior analysis in-the-Wild (ABAW) competition. We combine facial emotional descriptors extracted by pre-trained models, namely, our EmotiEffLib library, with acoustic features and embeddings of texts recognized from speech. The frame-level features are aggregated and fed into simple classifiers, e.g., multi-layered perceptron (feed-forward neural network with one hidden layer), to predict ambivalence/hesitancy and facial expressions. In the latter case, we also use the pre-trained facial expression recognition model to select high-score video frames and prevent their processing with a domain-specific video classifier. The video-level prediction of emotional mimicry intensity is implemented by simply aggregating frame-level features and training a multi-layered perceptron. Experimental results for three tasks from the ABAW challenge demonstrate that our approach significantly increases validation metrics compared to existing *** Codes 68T10 Copyright © 2025, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Deep neural networks and maximum likelihood search for approximate nearest neighbor in video-based image recognition

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Optical Memory and Neural networks (Information Optics) 2017年第2期26卷 129-136页

作者： Savchenko, A.V. National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

We analyzed the way to increase computational efficiency of video-based image recognition methods with matching of high dimensional feature vectors extracted by deep convolutional neural networks. We proposed an algorithm for approximate nearest neighbor search. At the first step, for a given video frame the algorithm verifies a reference image obtained when recognizing the previous frame. After that the frame is compared with a few number of reference images. Each next examined reference image is chosen so that to maximize conditional probability density of distances to the reference instances tested at previous steps. To decrease the required memory space we beforehand calculate only distances from all the images to small number of instances (pivots). When experimenting with either face photos from Labeled Faces in the Wild and PubFig83 datasets or with video data from YouTube Faces we showed that our algorithm allows accelerating the recognition procedure by 1.4–4 times comparing with known approximate nearest neighbor methods. © 2017, Allerton Press, Inc.

关键词： Face recognition

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Recommending Restaurants based on Classification of Photos from the Gallery of Mobile Device 20

Recommending Restaurants based on Classification of Photos f...

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20th IEEE Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2022

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

ISBN: (纸本)9781665489881

The restaurant recommender systems are important for travelers, but may suffer from the 'cold start' problem for new users. In this paper, it is assumed that photos of food in a gallery of mobile device of a user reflect his or her gastronomic preferences. The novel pipeline for modeling of the user's food preferences is introduced. At first, scene recognition neural network is used to extract photos of the restaurants from a gallery. It is proposed to train the multi-Task convolutional network to predict the presence of food in an input image and the attributes of the restaurant. The recognized types of cuisine are summarized in a profile of gastronomic preferences of the user. The restaurants in a given city are recommended based on these preferences and additional attributes of a restaurant such as cumulative rating. Experimental study for the Yelp datasets is provided. Several neural network architectures are compared, among which EfficientNet models demonstrated the best performance. © 2022 IEEE.

关键词： Recommender systems

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Efficient algorithms for Video-Based Engagement Prediction for a MOOC Course

Efficient Algorithms for Video-Based Engagement Prediction f...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Demochkina, Polina Savchenko, Andrey Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

In this paper, we address the engagement prediction task using a two-stage approach. First, two types of features are extracted from each frame on the input video using the OpenFace toolkit and an EfficientNet-based model that was pre-trained for the task of age, gender, and identity prediction. Each set of obtained feature vectors is then separately aggregated into a single video descriptor using statistical functions and a prediction is made using a linear support vector machine. Finally, the scores are fused, and the engagement intensity is predicted. The described ensemble model achieves MSE of 0.0534 on the test set of the EngageWild dataset from the EmotiW 2020 challenge. © 2022 IEEE.

关键词： Computer vision

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Multi-user facial emotion recognition in video based on user-dependent neural network adaptation 8

Multi-user facial emotion recognition in video based on user...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Churaev, Egor Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset. © 2022 IEEE.

关键词： Emotion Recognition

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Speaker-Aware Training of Speech Emotion Classifier with Speaker Recognition 23rd

Speaker-Aware Training of Speech Emotion Classifier with Spe...

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23rd International Conference on Speech and Computer, SPECOM 2021

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

ISBN: (纸本)9783030878016

In this paper, we discuss the possibility to improve the accuracy of speech emotion recognition in multi-user systems. We assume that a small corpus of speech emotional data is available for each speaker of interest. It is proposed to train a speaker-independent emotion classifier of arbitrary audio features including deep embeddings and fine-tune it on the utterances of each speaker. As a result, every user is associated with his or her own emotion recognition model. The concrete fine-tuned classifier may be chosen using a speaker recognition algorithm or even fixed if the identity of a user is known. It is experimentally shown that the proposed approach makes it possible to significantly improve the quality of conventional speaker-independent emotion classifier. © 2021, Springer Nature Switzerland AG.

关键词： Emotion Recognition

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Personalized Frame-Level Facial Expression Recognition in Video 3rd

Personalized Frame-Level Facial Expression Recognition in V...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

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

ISBN: (纸本)9783031090363

In this paper, the personalization of the video-based frame-level facial expression recognition is studied for multi-user systems if a small amount of short videos are available for each user. At first, embeddings of each video frame are computed using deep convolutional neural network pre-trained on a large emotional dataset of static images. Next, a dataset of videos is used to train a subject-independent emotion classifier, such as feed-forward neural network or frame attention network. Finally, it is proposed to fine-tune this neural classifier on the videos of each user of interest. As a result, every user is associated with his or her own emotional model. The classifier in a multi-user system is chosen by an appropriate video-based face recognition method. The experimental study with the RAMAS dataset demonstrates the significant (up to 25%) increase in accuracy of the proposed approach when compared to a subject-independent facial expression recognition. © 2022, Springer Nature Switzerland AG.

关键词： Face recognition

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Uncertainty of Graph Clustering in Correlation Block Model 22nd

Uncertainty of Graph Clustering in Correlation Block Model

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22nd International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2023

作者： Aroslankin, Artem Kalyagin, Valeriy Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (纸本)9783031432569

Cluster analysis is a powerful tool in network science and it is well developed in many directions. However, the uncertainty analysis of clustering algorithms is still not sufficiently investigated in the literature. To study uncertainty of clustering algorithms we propose to use a new model, which we call correlation block model. We suggest to measure uncertainty of clustering algorithms by error in cluster identification by observations. Uncertainty of different clustering algorithms are compared using proposed methodology. New and interesting phenomena are observed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors 9th

Efficient Group-Based Cohesion Prediction in Images Using Fa...

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9th International Conference on analysis of Images, Social networks, and Texts, AIST 2020

作者： Gavrikov, Ilya Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9783030712136

In this paper we study the problem of predicting the cohesiveness and emotion of a group of people in photo. We proposed a fast approach, consisting of face detection by using MTCNN, aggregation of facial features (age, gender and embeddings) extracted by multi-task MobileNet, prediction of group cohesion and classification of emotional background using multi-output convolution neural network. Experimental study on the Group Affect Dataset from EmotiW 2019 challenge demonstrated that our approach allows to achieve an improvement of quality and even to reduce the running time of an algorithm’s work when compared to known solutions. As a result, we obtained mean squared error 0.63 for cohesion prediction, which is 0.21 lower when compared to baseline CapsNet. © 2021, Springer Nature Switzerland AG.

关键词： Emotion Recognition

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