Temporal Label Hierachical Network for Compound Emotion Recognition Article Swipe
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.12973
The emotion recognition has attracted more attention in recent decades. Although significant progress has been made in the recognition technology of the seven basic emotions, existing methods are still hard to tackle compound emotion recognition that occurred commonly in practical application. This article introduces our achievements in the 7th Field Emotion Behavior Analysis (ABAW) competition. In the competition, we selected pre trained ResNet18 and Transformer, which have been widely validated, as the basic network framework. Considering the continuity of emotions over time, we propose a time pyramid structure network for frame level emotion prediction. Furthermore. At the same time, in order to address the lack of data in composite emotion recognition, we utilized fine-grained labels from the DFEW database to construct training data for emotion categories in competitions. Taking into account the characteristics of valence arousal of various complex emotions, we constructed a classification framework from coarse to fine in the label space.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.12973
- https://arxiv.org/pdf/2407.12973
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406058602
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406058602Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2407.12973Digital Object Identifier
- Title
-
Temporal Label Hierachical Network for Compound Emotion RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-17Full publication date if available
- Authors
-
Sunan Li, Hailun Lian, Cheng Lu, Yan Zhao, Tianhua Qi, Hao Yang, Yuan Zong, Wenming ZhengList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.12973Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.12973Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.12973Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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