Moving infrared small target detection presents significant challenges due to
tiny target sizes and low contrast against backgrounds. Currently-existing
methods primarily focus on extracting target features only from the
spatial-temporal domain. For further enhancing feature representation, more
information domains such as frequency are believed to be potentially valuable.
To extend target feature learning, we propose a new Triple-domain Strategy
(Tridos) with the frequency-aware memory enhancement on the spatial-temporal
domain. In our scheme, it effectively detaches and enhances frequency features
by a local-global frequency-aware module with Fourier transform. Inspired by
the human visual system, our memory enhancement aims to capture the target
spatial relations between video frames. Furthermore, it encodes temporal
dynamics motion features via differential learning and residual enhancing.
Additionally, we further design a residual compensation unit to reconcile
possible cross-domain feature mismatches. To our best knowledge, our Tridos is
the first work to explore target feature learning comprehensively in
spatial-temporal-frequency domains. The extensive experiments on three datasets
(DAUB, ITSDT-15K, and IRDST) validate that our triple-domain learning scheme
could be obviously superior to state-of-the-art ones. Source codes are
available at this https URL

以三个领域（时空域、频域和频率感知域）的目标特征学习为基础，提出了一种新的频率感知记忆增强方法（Tridos）来有效检测红外小目标，并通过实验证明其优于现有方法。

频率感知记忆增强下的动态红外小目标检测中的三域特征学习

Triple-domain Feature Learning with Frequency-aware Memory Enhancement  for Moving Infrared Small Target Detection

Test-time adaptation is a special setting of unsupervised domain adaptation
where a trained model on the source domain has to adapt to the target domain
without accessing source data. We propose a novel way to leverage
self-supervised contrastive learning to facilitate target feature learning,
along with an online pseudo labeling scheme with refinement that significantly
denoises pseudo labels. The contrastive learning task is applied jointly with
pseudo labeling, contrasting positive and negative pairs constructed similarly
as MoCo but with source-initialized encoder, and excluding same-class negative
pairs indicated by pseudo labels. Meanwhile, we produce pseudo labels online
and refine them via soft voting among their nearest neighbors in the target
feature space, enabled by maintaining a memory queue. Our method, AdaContrast,
achieves state-of-the-art performance on major benchmarks while having several
desirable properties compared to existing works, including memory efficiency,
insensitivity to hyper-parameters, and better model calibration. Project page:
sites.google.com/view/adacontrast.

本文提出 AdaContrast，通过自监督对比学习和在线伪标签提取相结合的方式，实现了无访问源域数据的测试时间自适应，并在主要基准测试中达到了最先进水平。