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IEEE/ICACT20260151 Slide.04        [Big Slide]	Oral Presentation
	The key contributions of this work can be summarized as follows: First, we propose MV-FER, a unified training paradigm that synthesizes three data views: original images, augmented flipped images with random erasing for view consistency, and AU-guided heatmaps for attention alignment. Second, we introduce a method for generating AU-guided heatmaps from facial landmarks and FACS theory, which serve as a signal to guide the spatial attention mechanisms of deep networks toward expression-based regions. We demonstrate consistent performance improvements over state-of-the-art methods on benchmark dataset RAF-DB, with ablation studies confirming the progressive gains from each view component.

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