In remote sensing, numerous unlabeled images are continuously accumulated over time, and it is difficult to annotate all the data. Therefore, a self-supervised learning technique that can improve the recognition rate using unlabeled data will be useful for remote sensing. This letter presents contrastive self-supervised learning with smoothed representation for remote sensing based on the SimCLR framework. In self-supervised learning for remote sensing, the well-known characteristic that images within a short distance might be semantically similar is usually used. Our algorithm is based on this knowledge, and it simultaneously utilizes several neighboring images as a positive pair of the anchor image, unlike existing methods such as Tile2Vec. Furthermore, MoCo and SimCLR, which are among the state-of-the-art self-supervised learning approaches, only use two augmented views of the single-input image, but our proposed approach uses multiple-input images and averages their representations (e.g., smoothed representation). Consequently, the proposed approach outperforms state-of-the-art self-supervised learning methods, such as Tile2Vec, MoCo, and SimCLR, in the cropland data layer (CDL), RESISC-45, UCMerced, and EuroSAT data sets. The proposed approach is comparable to the pretrained ImageNet model in the CDL classification task.

Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision.

In this article, we propose a novel model for facial micro-expression (FME) recognition. The proposed model basically comprises a transformer, which is recently used for computer vision and has never been used for FME recognition. A transformer requires a huge amount of data compared to a convolution neural network. Then, we use motion features, such as optical flow and late fusion to complement the lack of FME dataset. The proposed method was verified and evaluated using the SMIC and CASME II datasets. Our approach achieved state-of-the-art (SOTA) performance of 0.7447 and 73.17% in SMIC in terms of unweighted F1 score (UF1) and accuracy (Acc.), respectively, which are 0.31 and 1.8% higher than previous SOTA. Furthermore, UF1 of 0.7106 and Acc. of 70.68% were shown in the CASME II experiment, which are comparable with SOTA.

Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s. To derive micro-expressions, participants are asked to suppress their emotions as much as possible while watching emotion-inducing videos. However, it is a challenging process, and the number of samples collected tends to be less than those of macro-expressions. Because training models with insufficient data may lead to decreased performance, this study proposes two ways to solve the problem of insufficient data for micro-expression training. The first method involves N-step pre-training, which performs multiple transfer learning from action recognition datasets to those in the facial domain. Second, we propose Décalcomanie data augmentation, which is based on facial symmetry, to create a composite image by cutting and pasting both faces around their center lines. The results show that the proposed methods can successfully overcome the data shortage problem and achieve high performance.