1 code implementation • 13 Apr 2024 • Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders.
Ranked #1 on Dynamic Facial Expression Recognition on MAFW
Dynamic Facial Expression Recognition Facial Expression Recognition +2
no code implementations • 16 Nov 2023 • Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
Interestingly, we also observe that optimization of the unimodal branches improves the multimodal branch, compared to a similar multimodal model trained from scratch.
1 code implementation • 26 Jan 2022 • Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition.
Ranked #1 on Facial Emotion Recognition on RAVDESS
no code implementations • 26 Jan 2022 • Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this work, we propose several attention formulations for multivariate sequence data.
1 code implementation • 10 Nov 2021 • Firas Laakom, Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
Based on this idea, we propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend.
no code implementations • 9 Feb 2021 • Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
no code implementations • 11 Feb 2020 • Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj, Alexandros Iosifidis
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA).
1 code implementation • 2 May 2019 • Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process.
1 code implementation • 22 Apr 2019 • Kateryna Chumachenko, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju
In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives.