Efficient Hierarchical Domain Adaptive Thermal Infrared Tracking

Abstract

Constrained by the scarcity of labeled Thermal InfraRed (TIR) training data, current TIR trackers commonly rely on pre-trained RGB trackers. However, the domain discrepancy between TIR and RGB images limits effective utilization of RGB features, significantly degrades TIR tracking performance. To solve this challenge, we propose a hierarchical domain adaptation model to transfer useful pre-trained RGB features into TIR tracking more effective and efficient. Specifically, we first design a reflectance consistency network to learn style-invariant representations. Second, we present a target-aware adversarial network to align the target semantic features of the two domains. These two modules respectively narrow the distribution gap at the stylistic and semantic levels in a hierarchical manner. Third, to solve the inefficiency problem of domain adaptive training, we also propose a Bi-rank adapter side network to accelerate this process. While significantly reducing training time by 90%, our method achieves a new state-of-the-art on four TIR tracking benchmarks.