Deep-learning-based local feature extraction algorithms that combine
detection and description have made significant progress in visible image
matching. However, the end-to-end training of such frameworks is notoriously
unstable due to the lack of strong supervision of detection and the
inappropriate coupling between detection and description. The problem is
magnified in cross-modal scenarios, in which most methods heavily rely on the
pre-training. In this paper, we recouple independent constraints of detection
and description of multimodal feature learning with a mutual weighting
strategy, in which the detected probabilities of robust features are forced to
peak and repeat, while features with high detection scores are emphasized
during optimization. Different from previous works, those weights are detached
from back propagation so that the detected probability of indistinct features
would not be directly suppressed and the training would be more stable.
Moreover, we propose the Super Detector, a detector that possesses a large
receptive field and is equipped with learnable non-maximum suppression layers,
to fulfill the harsh terms of detection. Finally, we build a benchmark that
contains cross visible, infrared, near-infrared and synthetic aperture radar
image pairs for evaluating the performance of features in feature matching and
image registration tasks. Extensive experiments demonstrate that features
trained with the recoulped detection and description, named ReDFeat, surpass
previous state-of-the-arts in the benchmark, while the model can be readily
trained from scratch.

本文利用互相关联的检测和描述特点的相互加权策略进行多模态特征学习，提出了具有大触发范围的 Super Detector 和 learnable 非最大抑制层的 detector，并提出了可评估特征匹配和图像注册任务性能的基准系统，实验证明本文的方法可以显著提高匹配的准确性及训练的稳定性。