Medical image synthesis is pivotal in modern clinical workflows, addressing the issue of missing imaging modalities. While diffusion-based models have shown promise, existing approaches often neglect the rich clinical metadata, leading to synthesized images that lack semantic fidelity and fail to maintain strict consistency with the target modality. To address these challenges, we propose a metadata-guided diffusion bridge model, termed MetaDB, a novel framework that leverages textual clinical priors to steer the source-to-target translation process. Our method introduces two key innovations to ensure high-fidelity synthesis. First, we design a text-guided adaptive normalization layer, which dynamically modulates the feature statistics of the diffusion backbone using encoded clinical metadata. This mechanism explicitly aligns the synthesized features with the target modality’s attributes, ensuring semantic consistency throughout the generation process. Second, to prevent semantic degradation during the iterative denoising steps, we propose a semantics reconstruction network. This auxiliary module imposes a constraint that forces the network to preserve deep semantic representations, further reinforcing the semantic consistency between the generated output and the target description. Extensive experiments on multiple medical imaging datasets demonstrate that our approach achieves state-of-the-art performance in terms of quantitative metrics and visual quality, generating images that are both anatomically accurate and semantically faithful to clinical protocols.

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.