  Visual attention modeling, important for interpreting and prioritizing visual
stimuli, plays a significant role in applications such as marketing,
multimedia, and robotics. Traditional saliency prediction models, especially
those based on Convolutional Neural Networks (CNNs) or Transformers, achieve
notable success by leveraging large-scale annotated datasets. However, the
current state-of-the-art (SOTA) models that use Transformers are
computationally expensive. Additionally, separate models are often required for
each image type, lacking a unified approach. In this paper, we propose Saliency
Unification through Mamba (SUM), a novel approach that integrates the efficient
long-range dependency modeling of Mamba with U-Net to provide a unified model
for diverse image types. Using a novel Conditional Visual State Space (C-VSS)
block, SUM dynamically adapts to various image types, including natural scenes,
web pages, and commercial imagery, ensuring universal applicability across
different data types. Our comprehensive evaluations across five benchmarks
demonstrate that SUM seamlessly adapts to different visual characteristics and
consistently outperforms existing models. These results position SUM as a
versatile and powerful tool for advancing visual attention modeling, offering a
robust solution universally applicable across different types of visual
content.
