rPPG-VQA filters in-the-wild videos using signal-level SNR consensus and scene-level MLLM interference detection, then applies two-stage adaptive sampling to produce unsupervised rPPG models with substantially higher benchmark accuracy.
Multi- scale structural similarity for image quality assessment
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ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.
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rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Training
rPPG-VQA filters in-the-wild videos using signal-level SNR consensus and scene-level MLLM interference detection, then applies two-stage adaptive sampling to produce unsupervised rPPG models with substantially higher benchmark accuracy.
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ID-Sim: An Identity-Focused Similarity Metric
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.