Facial expression plays an important role in understanding human emotions. Most recently, deep learning based methods have shown promising for facial expression recognition. However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training. To solve this issue, prior works employ the pretrain-and-finetune strategy, i.e., utilize a large amount of unlabeled data to pretrain the network and then finetune it by the labeled data. As the labeled data is in a small amount, the final network performance is still restricted. From a different perspective, we propose to perform omni-supervised learning to directly exploit reliable samples in a large amount of unlabeled data for network training. Particularly, a new dataset is firstly constructed using a primitive model trained on a small number of labeled samples to select samples with high confidence scores from a face dataset, i.e., MS-Celeb-1M, based on feature-wise similarity. We experimentally verify that the new dataset created in such an omni-supervised manner can significantly improve the generalization ability of the learned FER model. However, as the number of training samples grows, computational cost and training time increase dramatically. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images, significantly improving the training efficiency. We have conducted extensive experiments on widely used benchmarks, where consistent performance gains can be achieved under various settings using the proposed framework. More importantly, the distilled dataset has shown its capabilities of boosting the performance of FER with negligible additional computational costs.
翻译:视觉表达方式在理解人类情感方面起着重要作用。 最近, 深层次的学习基础方法显示, 面部表情识别有希望。 但是, 目前最先进的面部表情识别( FER) 方法的性能与标签化的培训数据直接相关。 解决这个问题, 以前的工程使用大量未贴标签的预发和纤维内网战略, 即, 使用大量未贴标签的数据来预演网络, 然后用标签化数据微调。 由于标签化数据在数量上很小, 最后的网络性能仍然受到限制。 从不同的角度看, 我们建议进行全称式超超前的学习, 直接利用大量未贴标签的网络培训数据样本直接开发可靠的样本。 特别是, 之前的工程使用一个原始模型, 即使用少量标签化样本来选择具有高度自信的样本, 即 MS- Celeb- mlevy-M, 能够根据地貌相似性能进行。 我们实验性化了在这种模型中创建的新数据集成的模型化, 以低层次超低级的精确化模型化的实验性能, 提高我们所学的模型化数据的性能 。