  Multi-modal salient object detection (MSOD) aims to boost saliency detection
performance by integrating visible sources with depth or thermal infrared ones.
Existing methods generally design different fusion schemes to handle certain
issues or challenges. Although these fusion schemes are effective at addressing
specific issues or challenges, they may struggle to handle multiple complex
challenges simultaneously. To solve this problem, we propose a novel adaptive
fusion bank that makes full use of the complementary benefits from a set of
basic fusion schemes to handle different challenges simultaneously for robust
MSOD. We focus on handling five major challenges in MSOD, namely center bias,
scale variation, image clutter, low illumination, and thermal crossover or
depth ambiguity. The fusion bank proposed consists of five representative
fusion schemes, which are specifically designed based on the characteristics of
each challenge, respectively. The bank is scalable, and more fusion schemes
could be incorporated into the bank for more challenges. To adaptively select
the appropriate fusion scheme for multi-modal input, we introduce an adaptive
ensemble module that forms the adaptive fusion bank, which is embedded into
hierarchical layers for sufficient fusion of different source data. Moreover,
we design an indirect interactive guidance module to accurately detect salient
hollow objects via the skip integration of high-level semantic information and
low-level spatial details. Extensive experiments on three RGBT datasets and
seven RGBD datasets demonstrate that the proposed method achieves the
outstanding performance compared to the state-of-the-art methods. The code and
results are available at https://github.com/Angknpng/LAFB.
