  The detection of small infrared targets against blurred and cluttered
backgrounds has remained an enduring challenge. In recent years, learning-based
schemes have become the mainstream methodology to establish the mapping
directly. However, these methods are susceptible to the inherent complexities
of changing backgrounds and real-world disturbances, leading to unreliable and
compromised target estimations. In this work, we propose a bi-level adversarial
framework to promote the robustness of detection in the presence of distinct
corruptions. We first propose a bi-level optimization formulation to introduce
dynamic adversarial learning. Specifically, it is composited by the learnable
generation of corruptions to maximize the losses as the lower-level objective
and the robustness promotion of detectors as the upper-level one. We also
provide a hierarchical reinforced learning strategy to discover the most
detrimental corruptions and balance the performance between robustness and
accuracy. To better disentangle the corruptions from salient features, we also
propose a spatial-frequency interaction network for target detection. Extensive
experiments demonstrate our scheme remarkably improves 21.96% IOU across a wide
array of corruptions and notably promotes 4.97% IOU on the general benchmark.
The source codes are available at https://github.com/LiuZhu-CV/BALISTD.
