Emoticon: Context-aware multimodal emotion recognition using frege's principle
We present EmotiCon, a learning-based algorithm for context-aware perceived human
emotion recognition from videos and images. Motivated by Frege's Context Principle from …
emotion recognition from videos and images. Motivated by Frege's Context Principle from …
Multitask multi-database emotion recognition
MT Vu, M Beurton-Aimar… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
This work has been initiated for the 2nd Affective Behavior Analysis in-the-wild (ABAW 2021)
competition. We train a unified deep learning model on multi-databases to perform two …
competition. We train a unified deep learning model on multi-databases to perform two …
Progressive modality reinforcement for human multimodal emotion recognition from unaligned multimodal sequences
Human multimodal emotion recognition involves time-series data of different modalities,
such as natural language, visual motions, and acoustic behaviors. Due to the variable …
such as natural language, visual motions, and acoustic behaviors. Due to the variable …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
Emotion recognition for multiple context awareness
Understanding emotion in context is a rising hotspot in the computer vision community.
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Deep spatio-temporal feature fusion with compact bilinear pooling for multimodal emotion recognition
Multimodal emotion recognition has attracted great interest recently and numerous
methodologies have been successfully investigated. However, the task requires the effective …
methodologies have been successfully investigated. However, the task requires the effective …
Multi-clue fusion for emotion recognition in the wild
In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has
drawn more and more attention due to its huge potential applications. In the fourth …
drawn more and more attention due to its huge potential applications. In the fourth …
Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition
Multimodal signals are powerful for emotion recognition since they can represent emotions
comprehensively. In this article, we compare the recognition performance and robustness of …
comprehensively. In this article, we compare the recognition performance and robustness of …
HetEmotionNet: two-stream heterogeneous graph recurrent neural network for multi-modal emotion recognition
The research on human emotion under multimedia stimulation based on physiological
signals is an emerging field and important progress has been achieved for emotion …
signals is an emerging field and important progress has been achieved for emotion …
Multimodal multi-task learning for dimensional and continuous emotion recognition
Automatic emotion recognition is a challenging task which can make great impact on
improving natural human computer interactions. In this paper, we present our effort for the …
improving natural human computer interactions. In this paper, we present our effort for the …