View-Aware Salient Object Detection for $360^{\circ }$ Omnidirectional Image

Abstract

Image-based salient object detection (ISOD) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> scenarios is significant for understanding and applying panoramic information. However, research on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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.