Bodily Behaviour Recognition (BBR) and Eye Contact Detection (ECD) in multi-person group conversations are critical for understanding social dynamics, but traditional methods often rely solely on visual cues, lacking integration with semantic context. To address this, we propose a novel framework based on Large Vision-Language Models (LVLMs), leveraging their cross-modal alignment capability to fuse visual features (e.g., body postures, gaze directions) and linguistic semantics (e.g., behavioral category descriptions). A parameter-efficient tuning strategy using Low-Rank Adaptation (LoRA) is adopted, adapting only a subset of parameters in both the Language Model (LM) and Vision Transformer (ViT) modules, thus retaining pre-trained knowledge while reducing computational costs. The framework incorporates multi-task output heads to simultaneously predict BBR and ECD results. Experiments on the MPIIGroupInteraction dataset demonstrate superior performance: our method achieves 0.65 accuracy on BBR and 0.82 accuracy on ECD, outperforming state-of-the-art approaches by 0.02-0.03 in absolute terms. Ablation studies validate that applying LoRA to both LM and ViT with optimal hyperparameters yields the best results, confirming the importance of cross-modal synergy. This work highlights the potential of LVLMs in social behavior analysis, providing a lightweight and effective solution for understanding complex group interactions.

Micro-action refers to subtle, low-intensity non-verbal behaviors that can provide insights into an individual's underlying emotions and intentions. Due to its brief duration and significant overlap, identifying these micro-actions poses a challenge for current models. In response to these challenges, this paper proposes a novel multi-feature fusion framework, which extracts coarse-grained body features and fine-grained action features separately. Specifically, we present Temporal Contextualization for fine-grained learning, a cross-frame injection mechanism designed to capture essential spatio-temporal information and introduce a 3D-ResNet Adapter for coarse-grained learning, which aggregates temporal data and facilitates parameter-efficient fine-tuning. In consideration of the task dataset distribution's long-tail nature, the implementation of Feature Decoupling is undertaken, adopting a two-stage training strategy. By conducting experiments, the aforementioned hierarchical multi-feature extraction and aggregation approach has been demonstrated to yield substantial enhancement in Micro-Action Recognition. Our method attains an F1-mean score of 77.75% on the MA-52 dataset, ranking 1st in the 2nd Micro-Action Analysis Grand Challenge in Conjunction with ACM MM'25.

The rise of Multimodal Large Language Models (MLLMs) offers new opportunities for Micro-Expression (ME) analysis. This paper introduces Micro-Expression Visual Question Answering (ME-VQA), a novel task reformulating ME annotations (e.g., emotion categories, action units) into QA pairs. To address key challenges-hardware limitations, context inconsistency, and compositional reasoning gaps-we propose a Relationship-Aware Hierarchical VQA Framework. Our approach leverages mined emotion correlations (e.g., coarse-to-fine label dependencies) and employs a two-stage process: 1) Coarse-grained anchoring for broad emotion categories, and 2) Fine-grained reasoning constrained by coarse outputs and statistical rules. We further optimize efficiency via a dual-phase video sampling strategy: during training, keyframes (onset/apex/offset) and random non-expression frames are used; uniform sampling is applied at inference. Experiments demonstrate significant improvements in answer consistency and accuracy.

Estimating engagement in group interactions is crucial for building socially intelligent systems, such as in human-agent and human-robot interaction. However, precisely modeling the continuous frame-level fluctuations of engagement remains challenging, particularly when considering the complex multi-party signal interactions within groups. Our method employs an encoder that integrates BiLSTM with Transformer to effectively capture both local and global temporal dependencies of multimodal features. Crucially, we explicitly fuse signals from both the target participant and their conversational partners in group to model the holistic group interaction dynamics. Furthermore, we introduce an 8x overlapped optimized sliding window strategy, constructing a ''sliding pipeline'', which significantly enhances the temporal smoothness, continuity, and stability of predictions. In the final regression stage, we replace the traditional multilayer perceptron(MLP) decoder with Kolmogorov-Arnold Network (KAN), leveraging their superior function approximation capability to achieve more accurate engagement predictions. Evaluated on the test sets of NoXi-base, NoXi-addition, MPIIGroupInteraction and NoXi-J datasets from the Multimediate'25 Engagement Challenge, our approach demonstrates significant performance improvements, achieving highly competitive Concordance Correlation Coefficients (CCC) of 0.678 for global, approximately 56.1% higher than the baseline, which shows a significant improvement.