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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tao, Zhou | - |
| dc.contributor.author | Deng-Ping, Fan | - |
| dc.contributor.author | Geng, Chen | - |
| dc.date.accessioned | 2023-03-30T02:51:02Z | - |
| dc.date.available | 2023-03-30T02:51:02Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s41095-022-0268-6 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7314 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | SOD | vi |
| dc.subject | RGB | vi |
| dc.title | Specificity-preserving RGB-D saliency detection | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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