Qian Wang

SPWM cuts computational costs and energy consumption in image restoration tasks while maintaining high image quality, showcasing the untapped potential of spiking neural networks.

Uncovering critical security and privacy vulnerabilities in foundation-model-powered robots could redefine how we approach their deployment in real-world applications.

MIXGUARD achieves robust privacy protection in split learning without sacrificing model utility, outperforming existing defenses against advanced data reconstruction attacks.

VOID shatters the semantic structure of images to thwart unauthorized mimicry without sacrificing visual quality, achieving a 223% boost in defense efficacy.

By redefining action learning around semantic events, WALL-WM achieves unprecedented generalization across tasks and environments, outperforming traditional models.