Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement
Movable agents, such as autonomous vehicles and robots, should be able to autonomously adapt their pre-trained object recognition models to new environments without human annotations. While existing test-time adaptation (TTA) methods address label-free model adaptation, they typically treat test data as independent snapshots, without constructively exploiting a rich, unique source of supervision: the significant variance in prediction quality as an agent observes the same object from different distances and viewpoints. To capitalize on this, we propose \texttt{MoCaFe}, a model-agnostic and hyperparameter-insensitive paradigm that leverages motion-induced prediction variance for unsupervis...
| Authors | Yanan Ma, Yihang Tao, Zhengru Fang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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