Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method .

Abstract Team formation with realistic crowd simulation behavior is a challenge in computer graphics, multiagent control, and social simulation. In this study, we propose a framework of crowd formation via hierarchical planning, which includes cooperative‐task, coordinated‐behavior, and action‐control planning. In cooperative‐task planning, we improve the grid potential field to achieve global path planning for a team. In coordinated‐behavior planning, we propose a time–space table to arrange behavior scheduling for a movement. In action‐control planning, we combine the gaze‐movement angle model and fuzzy logic control to achieve agent action. Our method has several advantages. (1) The hierarchical architecture is guaranteed to match the human decision process from high to low intelligence. (2) The agent plans his behavior only with the local information of his neighbor; the global intelligence of the group emerges from these local interactions. (3) The time–space table fully utilizes three‐dimensional information. Our method is verified using crowds of various densities, from sparse to dense, employing quantitative performance measures. The approach is independent of the simulation model and can be extended to other crowd simulation tasks.

In this paper, we propose an approximate Voronoi base domain method to address constrained texture mapping problem. In particular, we generate approximate Voronoi base domains on the input texture image plane and planar embedding of mesh surface. Then, we propose to leverage the triangulations of constraint points on both sides, and yield the same topology for the two approximate Voronoi base domains. Based on the equivalent topology, we are able to employ generalized barycentric coordinates between pairwise Voronoi cells of the two approximate Voronoi base domains. The proposed method is highly efficient, and it allows to edit constraints in real time. Experimental results show the improved effectiveness and robustness of the proposed method for constraint texture mapping.

In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework. SJASM represents each review document in the form of opinion pairs, and can simultaneously model aspect terms and corresponding opinion words of the review for hidden aspect and sentiment detection. It also leverages sentimental overall ratings, which often come with online reviews, as supervision data, and can infer the semantic aspects and aspect-level sentiments that are not only meaningful but also predictive of overall sentiments of reviews. Moreover, we also develop efficient inference method for parameter estimation of SJASM based on collapsed Gibbs sampling. We evaluate SJASM extensively on real-world review data, and experimental results demonstrate that the proposed model outperforms seven well-established baseline methods for sentiment analysis tasks.

Online reviews are immensely valuable for customers to make informed purchase decisions and for businesses to improve the quality of their products and services. However, customer reviews grow exponentially while varying greatly in quality. It is generally very tedious and difficult, if not impossible, for users to read though the huge amount of review data. Fortunately, review quality evaluation enables a system to select the most helpful reviews for users' decision-making. Previous studies predict only the overall review utility about a product, and often focus on developing different data features to learn a quality function for addressing the problem. In this paper, we aim to select the most helpful reviews not only at the product level, but also at a fine-grained product aspect level. We propose a novel supervised joint aspect and sentiment model (SJASM), which is a probabilistic topic modeling framework that jointly discovers aspects and sentiments guided by a review helpfulness metric. One key advantage of SJASM is its ability to infer the underlying aspects and sentiments, which are indicative of the helpfulness of a review. We validate SJASM using publicly available review data, and our experimental results demonstrate the superiority of SJASM over several competing models.