Temporal Label Hierachical Network for Compound Emotion Recognition
<|reference_start|>Temporal Label Hierachical Network for Compound Emotion Recognition: 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.<|reference_end|>
@article{li2024temporal,
    title={Temporal Label Hierachical Network for Compound Emotion Recognition},
    author={Sunan Li and Hailun Lian and Cheng Lu and Yan Zhao and Tianhua Qi and
  Hao Yang and Yuan Zong and Wenming Zheng},
    journal={arXiv preprint arXiv:2407.12973},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12973},
    primaryClass={cs.CV cs.AI}
}