  In this paper, we present the solution to the Emotional Mimicry Intensity
(EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis
in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to
evaluate the emotional intensity of seed videos by assessing them from a set of
predefined emotion categories (i.e., "Admiration", "Amusement",
"Determination", "Empathic Pain", "Excitement" and "Joy"). To tackle this
challenge, we extracted rich dual-channel visual features based on ResNet18 and
AUs for the video modality and effective single-channel features based on
Wav2Vec2.0 for the audio modality. This allowed us to obtain comprehensive
emotional features for the audiovisual modality. Additionally, leveraging a
late fusion strategy, we averaged the predictions of the visual and acoustic
models, resulting in a more accurate estimation of audiovisual emotional
mimicry intensity. Experimental results validate the effectiveness of our
approach, with the average Pearson's correlation Coefficient($\rho$) across the
6 emotion dimensionson the validation set achieving 0.3288.
