Micro-expression (ME) is an instinctive reaction that is not controlled by thoughts. It reveals one's inner feelings, which is significant in sentiment analysis and lie detection. Since micro-expression is expressed as subtle facial changes within particular facial action units, learning discriminative and generalized features for Micro-expression Recognition (MER) is challenging. To achieve the purpose, this paper proposes a novel MER framework that simultaneously integrates supervised Prototype-based Memory Contrastive Learning (PMCL) for discriminative feature mining and adds Self-expression Reconstruction (SER) as an auxiliary task and regularization for better generalization. In particular, the proposed SER module is forced as a regularization by reconstructing input ME from the randomly dropped patch- wise features in the bottleneck. And, the PMCL module globally compares historical and current cluster agents learned from training instances to enhance intra-class compactness and inter-class separability. Extensive experiments are conducted on three benchmarks, e.g., SMIC, CASME II, and SAMM, under evaluation criteria of both Composite Database Evaluation (CDE) and Single Database Evaluation (SDE) protocols. The results show our method surpasses other state-of-the-art approaches under various evaluation metrics, achieving overall 86.30% unweighed F1-score and 88.30% unweighed average recall on the composite dataset. Furthermore, the ablation studies verify the effectiveness of our SER for better generalization and PMCL for better discrimination in learning feature representation from limited micro-expression samples.

With the tremendous performance increase of today's graphics technologies, visual details of digital humans in games, online virtual worlds, and virtual reality applications are becoming significantly more demanding. Hair is a vital component of a person's identity and can provide strong cues about age, background, and even personality. More and more researchers focus on hair modeling in the fields of computer graphics and virtual reality. Traditional methods are physics-based simulation by setting different parameters. The computation is expensive, and the constructing process is non-intuitive, difficult to control. Conversely, image-based methods have the advantages of fast modeling and high fidelity. This paper surveys the state of the art in the major topics of image-based techniques for hair modeling, including single-view hair modeling, static hair modeling from multiple images, video-based dynamic hair modeling, and the editing and reusing of hair modeling results. We first summarize the single-view approaches, which can be divided into the orientation-field and data-driven-based methods. The static methods from multiple images and dynamic methods are then reviewed in Sections III and IV. In Section V, we also review the editing and reusing of hair modeling results. The future development trends and challenges of image-based methods are proposed in the end.

In this paper, a modified YOLOv3 net has been proposed for surface defect detection. Different from other pixel-level segmenting methods, YOLOv3 locates the regions of surface defects with bounding rectangles. Compared with conventional detectors, the operating efficiency of YOLOv3 is rather high without generating region proposals by sliding boxes. Although pixel-level details of defects are omitted in the process, the primary information of the location of detects and class labels are extracted by YOLOv3 with high accuracy. This information is sufficient for surface defect inspection, and computational efficiency has been improved, simultaneously. To further light the structure of YOLOv3, loss function optimization and pruning strategy have been adopted in the original YOLOv3. The pruning ratio is determined by the tradeoff between detecting accuracy and computational efficiency. In our experiments, we compared the performance of modified YOLOv3 with several state-of-the-art methods, and modified YOLOv3 achieves the best performance on six types of surface defects in DAGM 2007 dataset.