Figure 1. Challenge and conceptual comparison of training-free 1-
shot domain generalization for object detection. (a) In specialized
domains (e.g., underwater or industrial), data can be very scarce
(e.g., only one labeled exemplar per class) and object boundaries
are ambiguous, making general-domain detectors fail under severe
domain gaps. (b) Training-based approaches rely on fine-tuning
with gradient updates, which incur extra cost, overfit under 1-shot
supervision, and remain unstable in high-IB domains. In contrast,
LAB-Det leverages language as a domain-invariant bridge: sup-
port exemplars are re-expressed as descriptive text and injected
into frozen vision–language models, enabling training-free, inter-
pretable, and robust target-domain detection.