I am delightful to show that my paper was accepted at the ACM MM 2024. This was my first ACM paper and was written by myself solely (solo author). Here is an abstract from the screenshot of the paper above.
Abstact
Automatic social perception recognition is a new task to mimic the measurement of human traits, which was previously done by humans via questionnaires. We evaluated unimodal and multimodal systems to predict agentive and communal traits from the LMU-ELP dataset. We optimized variants of recurrent neural networks from each feature from audio and video data and then fused them to predict the traits. Results on the development set show a consistent trend that multimodal fusion outperforms unimodal systems. The performance-weighted fusion also consistently outperforms mean and maximum fusions. We found two important factors that influence the performance of performance-weighted fusion. These factors are normalization and the number of models.
Once the link of the paper is available in ACM Library, I will put the link here.
Link: XXX.
Behind The Scene and Tips!
I have participating in MuSe challenge (Multimodal Sentiment Analysis Challenge and Workshop) for several years. This challenge, along with other challenges in conference like ICASSP and Interspeech, usually provides baseline program (code) and the respected dataset (e.g., ComParE). From this baseline, we can further analyze, make experiments, and often get new ideas to implement. My idea for that challenge (social perception challenge) is two parts: parameter optimization and multimodal fusion. I implement a lot of ideas (e.g., tuning more than 15 parameters) and some works. Once I get improvement with consistent results/phenomena (science must consistent!), I documented my work and submit a paper. This time, my paper got accepted!
