论文标题
Ciao！非普遍面部表达识别的对比适应机制
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition
论文作者
论文摘要
当前的面部表情识别系统需要与受过培训不同的情况时，需要昂贵的重新训练常规。将它们偏向学习特定的面部特征，而不是执行典型的转移学习方法，可能会帮助这些系统在不同任务中保持高性能，但会减少培训工作。在本文中，我们提出了对比度的抑制作用（CIAO），该机制适应了最后一层面部编码器以描述不同数据集上的特定情感特征。 CIAO在六个具有非常独特的情感表示形式的不同数据集上提高了面部表达识别性能的改善，特别是与最先进的模型相比。在我们的讨论中，我们对学习的高级面部特征的代表以及它们如何为每个单独的数据集的特征做出贡献。我们通过讨论CIAO如何在非普遍面部表情感知的最新发现范围内定位自己的研究来最终完成研究，及其对面部表达识别研究的影响。
Current facial expression recognition systems demand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning specific facial characteristics, instead of performing typical transfer learning methods, might help these systems to maintain high performance in different tasks, but with a reduced training effort. In this paper, we propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations, in particular when compared with state-of-the-art models. In our discussions, we make an in-depth analysis of how the learned high-level facial features are represented, and how they contribute to each individual dataset's characteristics. We finalize our study by discussing how CIAO positions itself within the range of recent findings on non-universal facial expressions perception, and its impact on facial expression recognition research.
