Label biases in facial expression recognition (FER) datasets, caused by annotators' subjectivity, pose challenges in improving the performance of target datasets when auxiliary labeled data are used. Moreover, training with multiple datasets can lead to visible degradations in the target dataset. To address these issues, we propose a novel framework called the AU-aware Vision Transformer (AU-ViT), which leverages unified action unit (AU) information and discards expression annotations of auxiliary data. AU-ViT integrates an elaborately designed AU branch in the middle part of a master ViT to enhance representation learning during training. Through qualitative and quantitative analyses, we demonstrate that AU-ViT effectively captures expression regions and is robust to real-world occlusions. Additionally, we observe that AU-ViT also yields performance improvements on the target dataset, even without auxiliary data, by utilizing pseudo AU labels. Our AU-ViT achieves performances superior to, or comparable to, that of the state-of-the-art methods on FERPlus, RAFDB, AffectNet, LSD and the other three occlusion test datasets.

Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues. Current approaches typically follow a two-stage pipeline: first localize subjects by off-the-shelf detectors, then perform emotion classification through the late fusion of subject and context features. However, the complicated paradigm suffers from disjoint training stages and limited fine-grained interaction between subject-context elements. To address the challenge, we present a single-stage emotion recognition approach, employing a Decoupled Subject-Context Transformer (DSCT), for simultaneous subject localization and emotion classification. Rather than compartmentalizing training stages, we jointly leverage box and emotion signals as supervision to enrich subject-centric feature learning. Furthermore, we introduce DSCT to facilitate interactions between fine-grained subject-context cues in a ''decouple-then-fuse'' manner. The decoupled query tokens-subject queries and context queries-gradually intertwine across layers within DSCT, during which spatial and semantic relations are exploited and aggregated. We evaluate our single-stage framework on two widely used context-aware emotion recognition datasets, CAER-S and EMOTIC. Our approach surpasses two-stage alternatives with fewer parameter numbers, achieving a 3.39% accuracy improvement and a 6.46% average precision gain on CAER-S and EMOTIC datasets, respectively. Code and models are available at: https://github.com/Sampson-Lee/DSCT.

The Multimodal Emotion Recognition (MER 2023) challenge aims to recognize emotion with audio, language, and visual signals, facilitating innovative technologies of affective computing. This paper presents our submission approach on the Semi-Supervised Learning Sub-Challenge (MER-SEMI). First, with large-scale unlabeled emotional videos, we train both image-based and video-based Masked Autoencoders to extract visual features, which termed as expression MAE (expMAE) for simplicity. The expMAE features are found to be largely complementary with other official baseline features. Second, since there is only a few labeled data, we use a classifier to generate pseudo labels for unlabeled videos which have high confidence for a certain category. In addition, we also explore several advanced large models for cross-feature extraction like CLIP, and apply factorized bilinear pooling (FBP) for multimodal feature fusion. Our methods finally achieved 88.55% in F1 score on MER-SEMI, ranking second place among all participating teams.