Zhang, S., Zhao, Z., & Guan, C. (2023). Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw59228.2023.00611
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the ASTAR - RIE2020 AME Programmatic Fund
Grant Reference no. : A20G8b0102