GEET: Gated Explicit-motion Extraction Tempo-spatial Adapter

Few-shot fine-grained (FS-FG) action recognition requires distinguishing visually similar actions that differ primarily in subtle temporal dynamics from only a few labeled examples. Existing foundation-model adaptations often rely on broad spatio-temporal updates, introducing excessive adaptation capacity for this setting. We propose **GEET** (**G**ated **E**xplicit-motion **E**xtraction **T**empo-spatial Adapter), a parameter-efficient adapter for a frozen CLIP backbone that prioritizes temporal specialization while limiting spatial adaptation. GEET separates appearance evolution from explicit frame-to-frame motion and combines them through gated temporal fusion, with lightweight spatial refinement grounding the explicit motion signal in locally coherent spatial evidence. This focused design requires only $\sim$3.8M tunable parameters and video input alone, GEET achieves state-of-the-art FS-FG recognition across diverse benchmarks with substantially lower latency and higher throughput, while remaining robust to diverse visual corruptions and temporal disruptions. Source code is attached in the suppl. material.