Researchers propose SD-ViT, a training framework that gives Vision Transformers a usable early exit at every block depth for face recognition, addressing the fixed-depth inference that limits deployment of ViTs in resource-constrained settings. The framework makes every depth produce discriminative embeddings, according to the paper's abstract. The work targets gallery-consistent early-exit face recognition, though the source excerpt provides no benchmark figures or named authors.
Vision Transformers (ViTs) perform strongly in face recognition (FR), yet their fixed-depth inference limits deployment in resource-constrained settings. We propose SD-ViT, a training framework that gives a usable exit at every block depth by making every depth produce discriminative embeddings, for
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