  Many targets are often very small in infrared images due to the long-distance
imaging meachnism. UNet and its variants, as popular detection backbone
networks, downsample the local features early and cause the irreversible loss
of these local features, leading to both the missed and false detection of
small targets in infrared images. We propose HintU, a novel network to recover
the local features lost by various UNet-based methods for effective infrared
small target detection. HintU has two key contributions. First, it introduces
the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge
of target locations to highlight critical local features. Second, it improves
the mainstream UNet-based architecture to preserve target pixels even after
downsampling. HintU can shift the focus of various networks (e.g., vanilla
UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels
to a more restricted area from the beginning. Experimental results on three
datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the
performance of existing methods with only an additional 1.88 ms cost (on RTX
Titan). Additionally, the explicit constraints of HintU enhance the
generalization ability of UNet-based methods. Code is available at
https://github.com/Wuzhou-Quan/HintU.
