  Infrared small target detection (IRSTD) has recently benefitted greatly from
U-shaped neural models. However, largely overlooking effective global
information modeling, existing techniques struggle when the target has high
similarities with the background. We present a Spatial-channel Cross
Transformer Network (SCTransNet) that leverages spatial-channel cross
transformer blocks (SCTBs) on top of long-range skip connections to address the
aforementioned challenge. In the proposed SCTBs, the outputs of all encoders
are interacted with cross transformer to generate mixed features, which are
redistributed to all decoders to effectively reinforce semantic differences
between the target and clutter at full scales. Specifically, SCTB contains the
following two key elements: (a) spatial-embedded single-head channel-cross
attention (SSCA) for exchanging local spatial features and full-level global
channel information to eliminate ambiguity among the encoders and facilitate
high-level semantic associations of the images, and (b) a complementary
feed-forward network (CFN) for enhancing the feature discriminability via a
multi-scale strategy and cross-spatial-channel information interaction to
promote beneficial information transfer. Our SCTransNet effectively encodes the
semantic differences between targets and backgrounds to boost its internal
representation for detecting small infrared targets accurately. Extensive
experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1k,
demonstrate that the proposed SCTransNet outperforms existing IRSTD methods.
Our code will be made public at https://github.com/xdFai.
