|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'503'724
Articles rated: 2609
23 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge | | Hoang Manh Hung
; Hyung-Jeong Yang
; Soo-Hyung Kim
; Guee-Sang Lee
; | | Date: |
28 Jul 2020 | | Abstract: | Emotion recognition has become a major problem in computer vision in recent
years that made a lot of effort by researchers to overcome the difficulties in
this task. In the field of affective computing, emotion recognition has a wide
range of applications, such as healthcare, robotics, human-computer
interaction. Due to its practical importance for other tasks, many techniques
and approaches have been investigated for different problems and various data
sources. Nevertheless, comprehensive fusion of the audio-visual and language
modalities to get the benefits from them is still a problem to solve. In this
paper, we present and discuss our classification methodology for MuSe-Topic
Sub-challenge, as well as the data and results. For the topic classification,
we ensemble two language models which are ALBERT and RoBERTa to predict 10
classes of topics. Moreover, for the classification of valence and arousal, SVM
and Random forests are employed in conjunction with feature selection to
enhance the performance. | | Source: | arXiv, 2007.13928 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER