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ArcAD reshapes cold-start anomaly detection by synthesizing pseudo-anomalies and clustering limited normal samples, leading to unprecedented performance gains.
Achieve nuanced control over portrait animations鈥攅ven for subtle states like thinking or drowsiness鈥攂y using hierarchical agent planning to translate high-level labels into precise eye movements.
By disentangling structure and motion in the latent space, CoWVLA achieves superior visuomotor learning compared to standard world-model and latent-action approaches.
Standard panoramic segmentation models crumble when faced with real-world camera rotations, but SO3UFormer maintains high accuracy even under arbitrary 3D reorientations by learning rotation-invariant spherical features.
VLMs can now excel at industrial anomaly detection by injecting domain-specific facts and aligning with expert preferences, achieving state-of-the-art zero-shot and one-shot performance.
