For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate between normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) a scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pre-trained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of adaptation to the target dataset.

Facial emotion recognition (FER) has been an active research topic in the past several years. One of difficulties in FER is the effective capture of geometrical and temporary information from landmarks. In this paper, we propose a graph convolution neural network that utilizes landmark features for FER, which we called a directed graph neural network (DGNN). Nodes in the graph structure were defined by landmarks, and edges in the directed graph were built by the Delaunay method. By using graph neural networks, we could capture emotional information through faces’ inherent properties, like geometrical and temporary information. Also, in order to prevent the vanishing gradient problem, we further utilized a stable form of a temporal block in the graph framework. Our experimental results proved the effectiveness of the proposed method for datasets such as CK+ (96.02%), MMI (69.4%), and AFEW (32.64%). Also, a fusion network using image information as well as landmarks, is presented and investigated for the CK+ (98.47% performance) and AFEW (50.65% performance) datasets.

This paper proposes various techniques that help Vision Transformer (ViT) to learn small-size datasets from scratch successfully. ViT, which applied the transformer structure to the image classification task, has outperformed convolutional neural networks, recently. However, the high performance of ViT results from pre-training using large-size datasets, and its dependence on large datasets comes from low locality inductive bias. And conventional ViT cannot effectively attend the target class due to redundant attention caused by a rather high constant temperature factor. In order to improve the locality inductive bias of ViT, this paper proposes novel tokenization (Shifted Patch Tokenization: SPT) using shifted patches and a position encoding (CoordConv Position Encoding: CPE) using 1×1 CoordConv. Also, to improve poor attention, we propose a new self-attention mechanism (Locality Self-Attention: LSA) based on learnable temperature and self-relation masking. SPT, CPE, and LSA are intuitive techniques, but they successfully improve the performance of ViT even on small-size datasets. We qualitatively show that each technique attends a more important area and contributes to having a flatter loss landscape. Moreover, the proposed techniques are generic add-on modules applicable to various ViT backbones. Our experiments show, when learning Tiny-ImageNet from scratch, the proposed scheme based on SPT, CPE, and LSA increases the accuracy of ViT backbones by +3.66 on average and up to +5.7. Also, the performance improvement of ViT backbones in ImageNet-1K classification, learning on COCO from scratch, and transfer learning on classification datasets verify that the generalization ability of the proposed method is excellent.