DART$^3$: Leveraging Distance for Test Time Adaptation in Person Re-Identification
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Person re-identification (ReID) models are known to suffer from camera bias,
where learned representations cluster according to camera viewpoints rather
than identity, leading to significant performance degradation under
(inter-camera) domain shifts in real-world surveillance systems when new
cameras are added to camera networks. State-of-the-art test-time adaptation
(TTA) methods, largely designed for classification tasks, rely on
classification entropy-based objectives that fail to generalize well to ReID,
thus making them unsuitable for tackling camera bias. In this paper, we
introduce DART$^3$, a TTA framework specifically designed to mitigate
camera-induced domain shifts in person ReID. DART$^3$ (Distance-Aware Retrieval
Tuning at Test Time) leverages a distance-based objective that aligns better
with image retrieval tasks like ReID by exploiting the correlation between
nearest-neighbor distance and prediction error. Unlike prior ReID-specific
domain adaptation methods, DART$^3$ requires no source data, architectural
modifications, or retraining, and can be deployed in both fully black-box and
hybrid settings. Empirical evaluations on multiple ReID benchmarks indicate
that DART$^3$ and DART$^3$ LITE, a lightweight alternative to the approach,
consistently outperforms state-of-the-art TTA baselines, making for a viable
option to online learning to mitigate the adverse effects of camera bias.
