activity
20202026
most citedAn Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

Elena Ryumina, Maxim Markitantov, Alexandr Axyonov +4

Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns,…

cs.CV2026

Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach

Elena Ryumina, Alexandr Axyonov, Dmitry Sysoev +4

Ambivalence/hesitancy recognition in unconstrained videos is a challenging problem due to the subtle, multimodal, and context-dependent nature of this behavioral state. In this pap…

cs.CV2025

Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach

Elena Ryumina, Maxim Markitantov, Alexandr Axyonov +3

Compound Expression Recognition (CER), a subfield of affective computing, aims to detect complex emotional states formed by combinations of basic emotions. In this work, we present…

cs.CV2024★ 1 cited

Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision

Elena Ryumina, Maxim Markitantov, Dmitry Ryumin +2

This paper presents the results of the SUN team for the Compound Expressions Recognition Challenge of the 6th ABAW Competition. We propose a novel audio-visual method for compound…

cs.LG2020★ 3 cited

An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild

Denis Dresvyanskiy, Elena Ryumina, Heysem Kaya +3

In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol.…