Paper
AULLM++: Structural Reasoning with Large Language Models for Micro-Expression Recognition
Authors
Zhishu Liu, Kaishen Yuan, Bo Zhao, Hui Ma, Zitong Yu
Abstract
Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affective cues. Previous methods face three key limitations: (1) heavy reliance on low-density visual information, rendering discriminative evidence vulnerable to background noise; (2) coarse-grained feature processing that misaligns with the demand for fine-grained representations; and (3) neglect of inter-AU correlations, restricting the parsing of complex expression patterns. We propose AULLM++, a reasoning-oriented framework leveraging Large Language Models (LLMs), which injects visual features into textual prompts as actionable semantic premises to guide inference. It formulates AU prediction into three stages: evidence construction, structure modeling, and deduction-based prediction. Specifically, a Multi-Granularity Evidence-Enhanced Fusion Projector (MGE-EFP) fuses mid-level texture cues with high-level semantics, distilling them into a compact Content Token (CT). Furthermore, inspired by micro- and macro-expression AU correspondence, we encode AU relationships as a sparse structural prior and learn interaction strengths via a Relation-Aware AU Graph Neural Network (R-AUGNN), producing an Instruction Token (IT). We then fuse CT and IT into a structured textual prompt and introduce Counterfactual Consistency Regularization (CCR) to construct counterfactual samples, enhancing the model's generalization. Extensive experiments demonstrate AULLM++ achieves state-of-the-art performance on standard benchmarks and exhibits superior cross-domain generalization.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.08387v1</id>\n <title>AULLM++: Structural Reasoning with Large Language Models for Micro-Expression Recognition</title>\n <updated>2026-03-09T13:45:21Z</updated>\n <link href='https://arxiv.org/abs/2603.08387v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.08387v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affective cues. Previous methods face three key limitations: (1) heavy reliance on low-density visual information, rendering discriminative evidence vulnerable to background noise; (2) coarse-grained feature processing that misaligns with the demand for fine-grained representations; and (3) neglect of inter-AU correlations, restricting the parsing of complex expression patterns. We propose AULLM++, a reasoning-oriented framework leveraging Large Language Models (LLMs), which injects visual features into textual prompts as actionable semantic premises to guide inference. It formulates AU prediction into three stages: evidence construction, structure modeling, and deduction-based prediction. Specifically, a Multi-Granularity Evidence-Enhanced Fusion Projector (MGE-EFP) fuses mid-level texture cues with high-level semantics, distilling them into a compact Content Token (CT). Furthermore, inspired by micro- and macro-expression AU correspondence, we encode AU relationships as a sparse structural prior and learn interaction strengths via a Relation-Aware AU Graph Neural Network (R-AUGNN), producing an Instruction Token (IT). We then fuse CT and IT into a structured textual prompt and introduce Counterfactual Consistency Regularization (CCR) to construct counterfactual samples, enhancing the model's generalization. Extensive experiments demonstrate AULLM++ achieves state-of-the-art performance on standard benchmarks and exhibits superior cross-domain generalization.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-09T13:45:21Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Zhishu Liu</name>\n </author>\n <author>\n <name>Kaishen Yuan</name>\n </author>\n <author>\n <name>Bo Zhao</name>\n </author>\n <author>\n <name>Hui Ma</name>\n </author>\n <author>\n <name>Zitong Yu</name>\n </author>\n </entry>"
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