论文标题
使用时尚和互补上下文信息影响野外表达行为分析
Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information
论文作者
论文摘要
野外面部表达识别（FER）对于建立可靠的人类计算机交互式系统至关重要。但是，当前的FER系统在各种自然和非控制条件下都无法表现良好。该报告介绍了我们提交的基于注意力的框架，用于表达识别情感行为分析（ABAW）2020年竞赛的表达识别轨迹。空间通道注意网（SCAN）用于提取本地和全球的专注特征，而无需从地标探测器中寻求任何信息。扫描是补充互补的上下文信息（CCI）分支，该信息使用有效的通道注意（ECA）来增强功能的相关性。该模型的性能在具有挑战性的AFF-WILD2数据集上进行了验证，以进行分类表达分类。
Facial expression recognition(FER) in the wild is crucial for building reliable human-computer interactive systems. However, current FER systems fail to perform well under various natural and un-controlled conditions. This report presents attention based framework used in our submission to expression recognition track of the Affective Behaviour Analysis in-the-wild (ABAW) 2020 competition. Spatial-channel attention net(SCAN) is used to extract local and global attentive features without seeking any information from landmark detectors. SCAN is complemented by a complementary context information(CCI) branch which uses efficient channel attention(ECA) to enhance the relevance of features. The performance of the model is validated on challenging Aff-Wild2 dataset for categorical expression classification.
