We propose SNAP, a novel low-latency Test-Time Adaptation framework that enables efficient model adaptation on edge devices by using sparse updates, significantly reducing computation while maintaining accuracy.

We propose a VLM-assisted dual-space adaptation framework to improve unsupervised domain adaptive hashing by calibrating pseudo-labels and decoupling feature and Hamming space alignment.