Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks and CLIP: Application to 8th ABAW Challenge
This work addresses facial expression analysis for computer vision applications, but it is incremental as it applies an existing method to a new challenge.
The authors tackled valence-arousal estimation, emotion recognition, and facial action unit detection in the 8th ABAW challenge, achieving results that surpassed the proposed baselines using the Dual-Direction Attention Mixed Feature Network (DDAMFN).
We present our contribution to the 8th ABAW challenge at CVPR 2025, where we tackle valence-arousal estimation, emotion recognition, and facial action unit detection as three independent challenges. Our approach leverages the well-known Dual-Direction Attention Mixed Feature Network (DDAMFN) for all three tasks, achieving results that surpass the proposed baselines. Additionally, we explore the use of CLIP for the emotion recognition challenge as an additional experiment. We provide insights into the architectural choices that contribute to the strong performance of our methods.