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FAS-R1 achieves unprecedented accuracy in face anti-spoofing by combining reasoning-oriented multi-task learning with innovative post-training techniques.
OracleAnalyser achieves superior analytical performance on oracle bone scripts with just 3 billion parameters, outperforming larger models.
Multi-agent collaboration and retrieval augmentation can overcome the limitations of static parametric memory in LLMs, enabling more nuanced and accurate multimodal emotion recognition.
Subtle visual signals, often overlooked, hold the key to unlocking hidden patterns across diverse applications, from security to medicine.
Explicitly modeling physiological frequency priors in rPPG unlocks significant improvements in signal recovery, even under challenging motion conditions that plague existing time-domain methods.
Gaze, often overlooked, reveals deepfake origins with surprising accuracy, enabling a new CLIP-based approach that significantly boosts deepfake attribution and detection.
