View-aware Salient Object Detection for 360{\deg} Omnidirectional Image
| العنوان: | View-aware Salient Object Detection for 360{\deg} Omnidirectional Image |
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| المؤلفون: | Junjie Wu, Changqun Xia, Tianshu Yu, Jia Li |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Signal Processing, Media Technology, Computer Science - Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computer Science Applications |
| الوصف: | 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. Comment: This paper has been accepted by IEEE Transactions on Multimedia |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ee67faf2aa176aca2e1cad183d06e05 http://arxiv.org/abs/2209.13222 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5ee67faf2aa176aca2e1cad183d06e05 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |