  RGB and Thermal (RGBT) Salient Object Detection (SOD) aims to achieve
high-quality saliency prediction by exploiting the complementary information of
visible and thermal image pairs, which are initially captured in an unaligned
manner. However, existing methods are tailored for manually aligned image
pairs, which are labor-intensive, and directly applying these methods to
original unaligned image pairs could significantly degrade their performance.
In this paper, we make the first attempt to address RGBT SOD for initially
captured RGB and thermal image pairs without manual alignment. Specifically, we
propose a Semantics-guided Asymmetric Correlation Network (SACNet) that
consists of two novel components: 1) an asymmetric correlation module utilizing
semantics-guided attention to model cross-modal correlations specific to
unaligned salient regions; 2) an associated feature sampling module to sample
relevant thermal features according to the corresponding RGB features for
multi-modal feature integration. In addition, we construct a unified benchmark
dataset called UVT2000, containing 2000 RGB and thermal image pairs directly
captured from various real-world scenes without any alignment, to facilitate
research on alignment-free RGBT SOD. Extensive experiments on both aligned and
unaligned datasets demonstrate the effectiveness and superior performance of
our method. The dataset and code are available at
https://github.com/Angknpng/SACNet.
