Facial expression recognition (FER) remains a challenging task in computer vision. Recent works have shown excellent performance in overall recognition accuracy, but its accuracy significantly decreases when recognizing similar expressions. This is due to interclass homogeneity and intraclass heterogeneity. To address these issues, we propose a novel dual‐stage network called DUAL, inspired by contrastive learning. First, we increase the distance between negative samples while reducing the distance between positive ones. This is achieved by dynamically updating pairs of comparison samples. Second, we introduce a two‐stage network architecture. The first stage uses two branches to extract image features and facial keypoint features. These branches interact to learn coarse‐grained features through mutual guidance. The second stage focuses on fine‐grained features using scale‐specific residual blocks. This allows the model to identify facial regions that are critical for recognizing expressions. We conducted extensive experiments on multiple datasets. The results show that DUAL surpasses state‐of‐the‐art models in items of performance. Additionally, the model shows high accuracy even in noisy conditions, highlighting its robustness.

Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space. For each sample, we generate heterogeneous representations and use view-consistency loss to make their representations consistent with each other. Multiview representation also inspires to supervise the pseudolabels generation by the aid of mutual supervision between views. In this article, we thus propose a view-consistent heterogeneous network (VCHN) to learn better representations by aligning view-agnostic semantics. Specifically, VCHN is constructed by constraining the predictions between two views so that the view pairs can supervise each other. To make the best use of cross-view information, we further propose a novel training strategy to generate more reliable pseudolabels, which thus enhances predictions of the VCHN. Extensive experimental results on three benchmark datasets demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.

Recently mathematical formula retrieval has become a hot research topic. The user requirements for mathematical formula retrieval vary widely in different application scenarios. In this paper we conduct a thorough analysis of the existing researches of mathematical formula retrieval and classify the functional requirements of the mathematical formula retrieval into eight types: Full Match, Partial Match, Semantic Match, Structural Match, Equivalent Match, Relative Calculation Match, Graph Match, and Combination Match. The connotations and denotations of the eight kinds of requirements are defined in this paper. Furthermore, we compare and analyze the current studies on mathematical formulas retrieval according to eight types of functional requirements. We finally give some suggestions on the potential technical improvements on mathematical formula retrieval.