Facial Expression Recognition(FER) is a key task in the broader landscape of affective computing and human-computer interaction, enabling machines to interpret human emotions. To better learn discriminative features under complex facial variations, recent FER research has increasingly adopted multi-branch fusion architectures that aim to capture complementary features from diverse perspectives. However, existing multi-branch fusion strategies, including static weighting, simple concatenation, or uncertainty-aware modeling, lack the capacity to comprehensively capture and reconcile the reliability variations across both individual instances and structural branches. To overcome these limitations, we propose a novel multi-branch fusion strategy, named Dual Uncertainty-Aware Fusion Framework(DUAFF), which improves the discriminability of integrated features by simultaneously modeling instance-wise uncertainty and inter-branch correlations. Specifically, the proposed method comprises two complementary modules: Instance-Discrepant Uncertainty-Aware Fusion Module (ID-UAFM) and Branch-Discrepant Uncertainty-Aware Fusion Module (BD-UAFM). ID-UAFM is introduced to perform channel-wise entropy analysis between semantically distinct samples to estimate instance-level uncertainty, enabling selective channel-wise fusion that emphasizes reliable representations while suppressing uncertain responses. BD-UAFM is further proposed to capture structural uncertainty by evaluating the relative reliability of features across multiple branches and adaptively weighting their contributions based on inter-branch discrepancies. Experimental results demonstrate that the proposed DUAFF consistently outperforms POSTER across three benchmark datasets, achieving accuracy improvements of 0.23 % on RAF-DB, 0.69 % on FER2013, and 0.29 % on AffectNet (7-class), thereby confirming its effectiveness in enhancing the reliability and discriminability of facial representations.

Multi-modal emotion recognition is crucial for human-computer interaction. Many existing algorithms attempt to achieve multi-modal interactions through a cross-attention mechanism. Due to the problems of noise introduction and heavy computation in the original attention mechanism, window attention has become a new trend. However, emotions are presented asynchronously between different modalities, which makes it difficult to interact with emotional information between windows. Furthermore, multi-modal data are temporally misaligned, so single fixed window size is hard to describe cross-modal information. In this paper, we put these two issues into a unified framework and propose the multi-granularity attention based Transformers (MGAT). It addresses the emotional asynchrony and modality misalignment issues through a multi-granularity attention mechanism. Experimental results confirm the effectiveness of our method and the state-of-the-art performance is achieved on IEMOCAP.

Speech emotion recognition plays an essential role in human-computer interaction. However, cross-individual representation learning and individual-agnostic systems are challenging due to the distribution deviation caused by individual differences. The existing related approaches mostly use the auxiliary task of speaker recognition to eliminate individual differences. Unfortunately, although these methods can reduce interindividual voiceprint differences, it is difficult to dissociate interindividual expression differences since each individual has its unique expression habits. In this paper, we propose an individual standardization network (ISNet) for speech emotion recognition to alleviate the problem of interindividual emotion confusion caused by individual differences. Specifically, we model individual benchmarks as representations of nonemotional neutral speech, and ISNet realizes individual standardization using the automatically generated benchmark, which improves the robustness of individual-agnostic emotion representations. In response to individual differences, we also propose more comprehensive and meaningful individual-level evaluation metrics. In addition, we continue our previous work to construct a challenging large-scale speech emotion dataset (LSSED). We propose a more reasonable division method of the training set and testing set to prevent individual information leakage. Experimental results on datasets of both large and small scales have proven the effectiveness of ISNet, and the new state-of-the-art performance is achieved under the same experimental conditions on IEMOCAP and LSSED.

Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, which has data collected from 820 subjects to simulate real- world distribution. In addition, we release some pre-trained models based on LSSED, which can not only promote the development of speech emotion recognition, but can also be transferred to related downstream tasks such as mental health analysis where data is extremely difficult to collect. Finally, our experiments show the necessity of large-scale datasets and the effectiveness of pre-trained models. The dateset will be released on https://github.com/tobefans/LSSED.