  Image-based salient object detection (ISOD) in 360{\deg} scenarios is
significant for understanding and applying panoramic information. However,
research on 360{\deg} ISOD has not been widely explored due to the lack of
large, complex, high-resolution, and well-labeled datasets. Towards this end,
we construct a large scale 360{\deg} ISOD dataset with object-level pixel-wise
annotation on equirectangular projection (ERP), which contains rich panoramic
scenes with not less than 2K resolution and is the largest dataset for
360{\deg} ISOD by far to our best knowledge. By observing the data, we find
current methods face three significant challenges in panoramic scenarios:
diverse distortion degrees, discontinuous edge effects and changeable object
scales. Inspired by humans' observing process, we propose a view-aware salient
object detection method based on a Sample Adaptive View Transformer (SAVT)
module with two sub-modules to mitigate these issues. Specifically, the
sub-module View Transformer (VT) contains three transform branches based on
different kinds of transformations to learn various features under different
views and heighten the model's feature toleration of distortion, edge effects
and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to
adjust the weights of different transform branches based on various sample
features and make transformed enhanced features fuse more appropriately. The
benchmark results of 20 state-of-the-art ISOD methods reveal the constructed
dataset is very challenging. Moreover, exhaustive experiments verify the
proposed approach is practical and outperforms the state-of-the-art methods.
