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Resolving visually indistinguishable human micro-actions requires more than raw foundation model scale: hierarchical coarse-to-fine soft fusion paired with targeted candidate reranking pushes long-tailed micro-action recognition to a benchmark-winning 79.99% F1-mean.
By using text as an anchor, this model achieves state-of-the-art emotional mimicry intensity estimation, even when visual and acoustic data are noisy or missing.
