HierMEQA: A Relationship-Aware Hierarchical Framework for Consistent Micro-Expression Visual Question Answering

摘要

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.