We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3, which employs the leader-follower attention. The second model has the same architecture for spatial and temporal encoding. As for the fusion block, it employs a compact and straightforward channel attention, borrowed from the End2You toolkit. Unlike our previous attempts that use Vggish feature directly as the audio feature, this time we feed the pre-trained VGG model using logmel-spectrogram and finetune it during the training. To make full use of the data and alleviate over-fitting, cross-validation is carried out. The code is available at https://github.com/sucv/ABAW3.
@article{arxiv.2303.10335,
title = {Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5},
author = {Su Zhang and Ziyuan Zhao and Cuntai Guan},
journal= {arXiv preprint arXiv:2303.10335},
year = {2023}
}
Comments
6 pages. 1 figure. arXiv admin note: substantial text overlap with arXiv:2203.13031