Recently, emotion recognition from facial expressions has achieved unprecedented accuracy with the development of deep learning. Despite this progress, most existing emotion recognition methods are supervised and thus require extensive annotation. This issue is particularly pronounced in continuous domain datasets where annotation costs are very high. Furthermore, discrete domain datasets containing specific poses are too uniform to reflect complex and actual emotions. Existing methods that employ classification loss pay little attention to image similarity, making it difficult to distinguish similar emotions. To improve the learning ability for image similarity and reduce the annotation cost of continuous domain datasets, this research proposes a Semi-Supervised Emotion Recognition (SSER) method, which incorporates Activation-matrix Triplet loss (AMT loss) and pseudo label with Complementary Information (CI label). Specifically, the AMT loss is constructed by encoding multiple activation channels of an image as a matrix, which are utilized to capture the image similarity. The CI label firstly adopts the coupling effect of the complementary information from images and the multi-stage model for SSL to obtain high-confidence pseudo-labels. Then, entropy minimization and consistency regularization are used to improve the accuracy of pseudo labels. The SSER is evaluated on continuous domain datasets (AFEW-VA and AFF-Wild) and discrete domain datasets (FER2013 and CK+). The experimental results demonstrate that the SSER combined with AMT loss and CI label makes improvement for emotion recognition on continuous domain datasets, meanwhile the SSER is also desirable and effective for emotion recognition on discrete domain datasets.

在一致性正则化与熵最小化的基础上提出一种新的半监督学习算法Mean Mixup,集成数据的互补信息,然后使用熵最小化给未标记数据生成可靠的伪标签,在一致性正则化下进一步优化模型分类结果.在常用数据集SVHN和CIFAR10上对Mean Mixup算法进行了评估,实验结果表明,所提出的方法在分类准确率上优于一些已有的半监督学习算法.

立足于深度学习,提出面向细粒度图像的自适应三元组网络的鲁棒图像检索算法.首先,提出的视觉显著性检测方法被用来去除图像噪音,以便提取图像中目标主体辨识度更高的深度特征;然后,添加特征增强模块来提高深度特征的表征能力和鲁棒性;最后设计三元组网络,弥补传统分类模型特征判别能力不足的缺陷,获取更适用于细粒度图像检索的网络模型.经实验验证,采用视觉显著性检测、特征增强模块和自适应三元组损失函数方法构建的网络模型提取的深度特征不仅加快检索效率,同时也提高了检索精度.