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arXiv Computer Vision By Atif Belal, Lilian Hollard, Marco Pedersoli, Eric Granger

DESA-TTA: Dynamic EMA and Source Anchoring for Test-Time Adaptation

Read the original on arXiv Computer Vision →

DESA‑TTA introduces a low‑overhead test‑time adaptation technique for vision‑language object detectors that dynamically adjusts the teacher’s exponential moving average coefficient based on pseudo‑label confidence and box density. It also employs source anchoring to gradually pull student parameters back toward their pretrained values, mitigating cumulative drift. Experiments on multiple distribution shifts and two VLOD architectures demonstrate consistent gains, with a 14.5‑point AP₅₀ improvement on VOC‑C and a 55% higher inference throughput compared to the prior state‑of‑the‑art TTA method for YOLO‑World.

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