Year: 2023
Type: preprint
Abstract: This paper introduces our method for the Emotional Reaction Intensity (ERI) Estimation Challenge, in CVPR 2023: 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Based on the multimodal data provided by the originazers, we extract acoustic and visual features with different pretrained models. The multimodal features are mixed together by Transformer Encoders with cross-modal attention mechnism. In this paper, 1. better features are extracted with the SOTA pretrained models. 2. Compared with the baseline, we improve the Pearson's Correlations Coefficient a lot. 3. We process the data with some special skills to enhance performance ability of our model.
Source: arXiv (Cornell University)
Authors: Wang, Shangfei, Wu, Jiaqiang, Zheng, Feiyi, Li, Xin, Li, Xuewei, Wang, Suwen, Wu, Yi, Chang, Yanan, Miao, Xiangyu
Institution: University of Science and Technology of China
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4327842008