  In this paper, we present a novel methodology we call MDS-ViTNet (Multi
Decoder Saliency by Vision Transformer Network) for enhancing visual saliency
prediction or eye-tracking. This approach holds significant potential for
diverse fields, including marketing, medicine, robotics, and retail. We propose
a network architecture that leverages the Vision Transformer, moving beyond the
conventional ImageNet backbone. The framework adopts an encoder-decoder
structure, with the encoder utilizing a Swin transformer to efficiently embed
most important features. This process involves a Transfer Learning method,
wherein layers from the Vision Transformer are converted by the Encoder
Transformer and seamlessly integrated into a CNN Decoder. This methodology
ensures minimal information loss from the original input image. The decoder
employs a multi-decoding technique, utilizing dual decoders to generate two
distinct attention maps. These maps are subsequently combined into a singular
output via an additional CNN model. Our trained model MDS-ViTNet achieves
state-of-the-art results across several benchmarks. Committed to fostering
further collaboration, we intend to make our code, models, and datasets
accessible to the public.
