We present our contribution to the 7th ABAW challenge at ECCV 2024, by utilizing a Dual-Direction Attention Mixed Feature Network (DDAMFN) for multitask facial expression recognition, we achieve results far beyond the proposed baseline for the Multi-Task ABAW challenge. Our proposal uses the well-known DDAMFN architecture as base to effectively predict valence-arousal, emotion recognition, and facial action units. We demonstrate the architecture ability to handle these tasks simultaneously, providing insights into its architecture and the rationale behind its design. Additionally, we compare our results for a multitask solution with independent single-task performance.

翻译：本文介绍了我们在ECCV 2024第七届ABAW挑战赛中的研究成果。通过采用双方向注意力混合特征网络（DDAMFN）进行多任务面部表情识别，我们在多任务ABAW挑战中取得了远超基准模型的性能。该方案以经典的DDAMFN架构为基础，能够有效预测效价-唤醒度、情绪识别和面部动作单元。我们验证了该架构同时处理这些任务的能力，并深入剖析了其结构设计原理与内在机制。此外，我们还对比了多任务解决方案与独立单任务模型的性能表现。