Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition

Facial expression data is characterized by a significant imbalance, with most collected data showing happy or neutral expressions and fewer instances of fear or disgust. This imbalance poses challenges to facial expression recognition (FER) models, hindering their ability to fully understand various human emotional states. Existing FER methods typically report overall accuracy on highly imbalanced test sets but exhibit low performance in terms of the mean accuracy across all expression classes. In this paper, our aim is to address the imbalanced FER problem. Existing methods primarily focus on learning knowledge of minor classes solely from minor-class samples. However, we propose a novel approach to extract extra knowledge related to the minor classes from both major and minor class samples. Our motivation stems from the belief that FER resembles a distribution learning task, wherein a sample may contain information about multiple classes. For instance, a sample from the major class surprise might also contain useful features of the minor class fear. Inspired by that, we propose a novel method that leverages re-balanced attention maps to regularize the model, enabling it to extract transformation invariant information about the minor classes from all training samples. Additionally, we introduce re-balanced smooth labels to regulate the cross-entropy loss, guiding the model to pay more attention to the minor classes by utilizing the extra information regarding the label distribution of the imbalanced training data. Extensive experiments on different datasets and backbones show that the two proposed modules work together to regularize the model and achieve state-of-the-art performance under the imbalanced FER task. Code is available at https://github.com/zyh-uaiaaaa.Comment: Accepted by NeurIPS202

SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation

In recent years, vision transformers have been introduced into face recognition and analysis and have achieved performance breakthroughs. However, most previous methods generally train a single model or an ensemble of models to perform the desired task, which ignores the synergy among different tasks and fails to achieve improved prediction accuracy, increased data efficiency, and reduced training time. This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation (40 attributes including gender) based on a single Swin Transformer. Our design, the SwinFace, consists of a single shared backbone together with a subnet for each set of related tasks. To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks. Extensive experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks. Especially, it achieves 90.97% accuracy on RAF-DB and 0.22 $\epsilon$-error on CLAP2015, which are state-of-the-art results on facial expression recognition and age estimation respectively. The code and models will be made publicly available at https://github.com/lxq1000/SwinFace

A Single Amino Acid Substitution in RFC4 Leads to Endoduplication and Compromised Resistance to DNA Damage in <i>Arabidopsis thaliana</i>

Replication factor C (RFC) is a heteropentameric ATPase associated with the diverse cellular activities (AAA+ATPase) protein complex, which is composed of one large subunit, known as RFC1, and four small subunits, RFC2/3/4/5. Among them, RFC1 and RFC3 were previously reported to mediate genomic stability and resistance to pathogens in Arabidopsis. Here, we generated a viable rfc4e (rfc4−1/RFC4G54E) mutant with a single amino acid substitution by site-directed mutagenesis. Three of six positive T2 mutants with the same amino acid substitution, but different insertion loci, were sequenced to identify homozygotes, and the three homozygote mutants showed dwarfism, early flowering, and a partially sterile phenotype. RNA sequencing revealed that genes related to DNA repair and replication were highly upregulated. Moreover, the frequency of DNA lesions was found to be increased in rfc4e mutants. Consistent with this, the rfc4e mutants were very sensitive to DSB-inducing genotoxic agents. In addition, the G54E amino acid substitution in AtRFC4 delayed cell cycle progression and led to endoduplication. Overall, our study provides evidence supporting the notion that RFC4 plays an important role in resistance to genotoxicity and cell proliferation by regulating DNA damage repair in Arabidopsis thaliana