Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition

In recent years, Facial Expression Recognition (FER) has gained increasing attention. Most current work focuses on supervised learning, which requires a large amount of labeled and diverse images, while FER suffers from the scarcity of large, diverse datasets and annotation difficulty. To address these problems, we focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations to learn features of facial geometry and expression regions, followed by two-stage fine-tuning on FER datasets with limited labels. In addition, to further alleviate the scarcity of labeled and diverse images, we propose a Mixup-based data augmentation strategy tailored for facial images, and the loss weights of real and virtual images are determined according to the intersection-over-union (IoU) of the faces in the two images. Experiments on RAF-DB, AffectNet, and FERPlus show that our method outperforms existing semi-supervised FER methods and achieves new state-of-the-art performance. Remarkably, with only 5%, 25% training sets,our method achieves 64.02% on AffectNet,and 88.23% on RAF-DB, which is comparable to fully supervised state-of-the-art methods. Codes will be made publicly available at https://github.com/zhelishisongjie/SSFER.

Advancing Biomedical Text Mining with Community Challenges

The field of biomedical research has witnessed a significant increase in the accumulation of vast amounts of textual data from various sources such as scientific literatures, electronic health records, clinical trial reports, and social media. However, manually processing and analyzing these extensive and complex resources is time-consuming and inefficient. To address this challenge, biomedical text mining, also known as biomedical natural language processing, has garnered great attention. Community challenge evaluation competitions have played an important role in promoting technology innovation and interdisciplinary collaboration in biomedical text mining research. These challenges provide platforms for researchers to develop state-of-the-art solutions for data mining and information processing in biomedical research. In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining. Firstly, we collect the information of these evaluation tasks, such as data sources and task types. Secondly, we conduct systematic summary and comparative analysis, including named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. Then, we summarize the potential clinical applications of these community challenge tasks from translational informatics perspective. Finally, we discuss the contributions and limitations of these community challenges, while highlighting future directions in the era of large language models.