EmbodiTTA: Resource-Efficient Test-Time Adaptation for Embodied Visual Systems

Abstract:Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy, existing CTTA approaches present poor real-world applicability on resource-constrained edge devices, due to the substantial memory overhead and energy consumption. In this work, we first introduce a novel paradigm -- on-demand TTA -- which triggers adaptation only when a significant domain shift is detected. Then, we present OD-TTA, an on-demand TTA framework for accurate and efficient adaptation on edge devices. OD-TTA comprises three innovative techniques: 1) a lightweight domain shift detection mechanism to activate TTA only when it is needed, drastically reducing the overall computation overhead, 2) a source domain selection module that chooses an appropriate source model for adaptation, ensuring high and robust accuracy, 3) a decoupled Batch Normalization (BN) update scheme to enable memory-efficient adaptation with small batch sizes. Extensive experiments show that OD-TTA achieves comparable and even better performance while reducing the energy and computation overhead remarkably, making TTA a practical reality.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2505.00986 [cs.LG]

(or arXiv:2505.00986v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2505.00986 Focus to learn more arXiv-issued DOI via DataCite

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