Search Results for author: Kateryna Chumachenko

Found 9 papers, 4 papers with code

Improving Unimodal Inference with Multimodal Transformers

no code implementations16 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.

Emotion Recognition Hand Gesture Recognition +2

Self-attention fusion for audiovisual emotion recognition with incomplete data

1 code implementation26 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.

Facial Emotion Recognition

Self-Attention Neural Bag-of-Features

no code implementations26 Jan 2022 Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj

In this work, we propose several attention formulations for multivariate sequence data.

Learning to ignore: rethinking attention in CNNs

1 code implementation10 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.

Ensembling object detectors for image and video data analysis

no code implementations9 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.

Object object-detection +1

Incremental Fast Subclass Discriminant Analysis

no code implementations11 Feb 2020 Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj, Alexandros Iosifidis

This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA).

Speed-up and multi-view extensions to Subclass Discriminant Analysis

1 code implementation2 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.

Graph Embedding regression

Machine Learning Based Analysis of Finnish World War II Photographers

1 code implementation22 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.

BIG-bench Machine Learning

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