Automatic real personality recognition (RPR) aims to evaluate human real personality traits from their expressive behaviours. However, most existing solutions generally act as external observers to infer observers' personality impressions based on target individuals' expressive behaviours, which significantly deviate from their real personalities and consistently lead to inferior recognition performance. Inspired by the association between real personality and human internal cognition underlying the generation of expressive behaviours, we propose a novel RPR approach that efficiently simulates personalised internal cognition from external short audio-visual behaviours expressed by target individual. The simulated personalised cognition, represented as a set of network weights that enforce the personalised network to reproduce the individual-specific facial reactions, is further encoded as a graph containing two-dimensional node and edge feature matrices, with a novel 2D Graph Neural Network (2D-GNN) proposed for inferring real personality traits from it. To simulate real personality-related cognition, an end-to-end (E2E) strategy is designed to jointly train our cognition simulation, 2D graph construction, and personality recognition modules. Experiments show our approach’s effectiveness in capturing real personality traits with superior computational efficiency.

Existing video-based automatic depression assessment (ADA) approaches frequently achieve video-level depression assessment by aggregating features or predictions of individual frames or equal-length segments within the given video. While their performances have been largely enhanced by recent advanced deep learning models, they typically fail to explicitly consider the varied importance of depression-related behavioural cues across different video segments, i.e., segments within one video may contain behaviours reflecting varying levels of depression. Underestimating segment-level variations can obscure the detection of facial behaviour cues associated with depression, thereby undermining the accuracy and interpretability of video-based depression detection systems. In this paper, we propose a novel video-based ADA approach that specifically identifies and differentiates video segments that exhibit depression-related facial behaviours across varying temporal durations, providing clear insights into how each segment contributes to the video-level depression prediction. To achieve this, a novel weakly supervised strategy is proposed to compare segment-level behaviours with video-level depression label, enabling the model to assign depression-relevant scores to multiple temporal scale video segments and attend selectively to those most indicative of depressive states. Extensive experiments on the AVEC 2013 and AVEC 2014 face video depression datasets demonstrate the effectiveness of our approach.

In dyadic human-human interactions, individuals may express multiple different facial reactions in response to the same/similar behaviours expressed by their conversational partners depending on their personalised behaviour patterns. As a result, frequently-employed reconstruction loss-based strategies lead the training of previous automatic facial reaction generation (FRG) models to not only suffer from the 'one-to-many mapping' problem, but also fail to comprehensively consider the quality of the generated facial reactions. Besides, none of them considered such personalised behaviour patterns in generating facial reactions. In this paper, we propose the first adversarial FRG model training strategy which jointly learns appropriateness and realism discriminators to provide comprehensive task-specific supervision for training the target facial reaction generators, and reformulates the 'one-to-many (facial reactions) mapping' training problem as a 'one-to-one (distribution) mapping' training task, i.e., the FRG model is trained to output a distribution representing multiple appropriate/plausible facial reaction from each input human behaviour. In addition, our approach also serves as the first offline FRG approach that considers personalised behaviour patterns in generating of target individuals' facial reactions. Experiments show that our PerReactor not only largely outperformed all existing offline solutions for generating more appropriate, diverse and realistic facial reactions, but also is the first approach that can effectively generate personalised appropriate facial reactions.

Multimodal emotion recognition systems rely heavily on the full availability of modalities, suffering significant performance declines when modal data is incomplete. To tackle this issue, we present the Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) framework, an innovative approach that sequentially engages in cross-modal alignment, reconstruction, and refinement phases to handle missing modalities and enhance emotion recognition. This framework utilizes unsupervised distribution-based contrastive learning to align heterogeneous modal distributions, reducing discrepancies and modeling semantic uncertainty effectively. The reconstruction phase applies normalizing flow models to transform these aligned distributions and recover missing modalities. The refinement phase employs supervised point-based contrastive learning to disrupt semantic correlations and accentuate emotional traits, thereby enriching the affective content of the reconstructed representations. Extensive experiments confirm the superior performance of CM-ARR. Notably, averaged across six scenarios of missing modalities, CM-ARR achieves absolute improvements of 2.11%/2.12% (WAR/UAR), and 1.71%/1.96% (WAR/UAR), respectively, on IEMOCAP and MSP-IMPROV datasets.

In dyadic interactions, a broad spectrum of human facial reactions might be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023 and REACT 2024 challenges, we are proposing the REACT 2025 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can be used to generate multiple appropriate, diverse, realistic and synchronised human-style facial reactions expressed by human listeners in response to an input stimulus (i.e., audio-visual behaviours expressed by their corresponding speakers). As a key of the challenge, we provide challenge participants with the first natural and large-scale multi-modal Multiple Appropriate Facial Reaction Generation (MAFRG) dataset (called MARS) recording 136 human-human dyadic interactions containing a total of 2856 interaction sessions covering five different topics. In addition, this paper also presents the challenge guidelines and the performance of our baselines on the two proposed sub-challenges: Offline MAFRG and Online MAFRG, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2025

We present SemiCRL, a novel framework for volumetric medical image segmentation that formulates an innovative contrastive learning methodology in a semi-supervised learning setting. We leverage the pseudo-labels generated in semi-supervised learning to guide the selection of negative samples for our contrastive learning, aiming to alleviate the class collision issue and learn enhanced class-discriminative latent representations. However, to address the inaccuracies in pseudo-labels, which stem from the empirical distribution misalignment between labeled and unlabeled data, we introduce a pseudo-label refinement strategy based on class prototypes computed from learned latent representations. Furthermore, our contrastive learning utilizes class prototypes as powerful reference points to enforce the alignment of latent-space distribution of labeled and unlabeled data, thus fostering knowledge transfer from labeled to unlabeled data, which in turn enhances the generation of accurate pseudo-labels in semi-supervised learning. Experiments on two public medical image datasets demonstrate our proposed method outperforms existing state-of-the-art semi-supervised approaches.

Human facial reactions play crucial roles in dyadic human-human interactions, where individuals (i.e., listeners) with varying cognitive process styles may display different but appropriate facial reactions in response to an identical behaviour expressed by their conversational partners. While several existing facial reaction generation approaches are capable of generating multiple appropriate facial reactions (AFRs) in response to each given human behaviour, they fail to take human's personalised cognitive process in AFRs generation. In this paper, we propose the first online personalised multiple appropriate facial reaction generation (MAFRG) approach which learns a unique personalised cognitive style from the target human listener's previous facial behaviours and represents it as a set of network weight shifts. These personalised weight shifts are then applied to edit the weights of a pre-trained generic MAFRG model, allowing the obtained personalised model to naturally mimic the target human listener's cognitive process in its reasoning for multiple AFRs generations. Experimental results show that our approach not only largely outperformed all existing approaches in generating more appropriate and diverse generic AFRs, but also serves as the first reliable personalised MAFRG solution. Our code is made available at https://github.com/xk0720/PerFRDiff.