The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available.

翻译：可见光谱数据集的利用使得深度网络取得了显著成功。然而，真实任务通常包含低光照条件，这给基于大规模RGB图像数据集训练的模型带来了性能瓶颈。热红外相机对此类条件具有更强的鲁棒性，因此在真实应用中使用热成像具有实用价值。无监督域自适应（UDA）允许将信息从源域迁移到完全无标签的目标域。尽管UDA取得了实质性改进，但其与监督学习之间的性能差距仍然显著。通过挑选少量目标样本进行标注并用于训练，主动域自适应旨在以最小标注成本缩小这一差距。为了检验结合可见光谱与热成像模态的效率，我们提出了一种主动域自适应方法。当域间差异如可见光到热成像任务中那样显著时，可以推断未采用显式域对齐的方法无法充分发挥其潜力。为此，我们提出了一种光谱迁移引导的主动域自适应方法，能够在对齐源域和目标域的同时，选择信息量最大的无标签目标样本。我们使用大规模可见光谱数据集MS-COCO作为源域，热成像数据集FLIR ADAS作为目标域，展示了我们方法的结果。大量实验评估表明，我们提出的方法优于最先进的主动域自适应方法。代码和模型均已公开。