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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2508.19746 |
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| _version_ | 1866912556237455360 |
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| author | Xu, Qiyao Wu, Qiming Li, Xiaowei |
| author_facet | Xu, Qiyao Wu, Qiming Li, Xiaowei |
| contents | Segment Anything Model (SAM) has demonstrated remarkable capabilities in solving light field salient object detection (LF SOD). However, most existing models tend to neglect the extraction of prompt information under this task. Meanwhile, traditional models ignore the analysis of frequency-domain information, which leads to small objects being overwhelmed by noise. In this paper, we put forward a novel model called self-prompting light field segment anything model (SPLF-SAM), equipped with unified multi-scale feature embedding block (UMFEB) and a multi-scale adaptive filtering adapter (MAFA). UMFEB is capable of identifying multiple objects of varying sizes, while MAFA, by learning frequency features, effectively prevents small objects from being overwhelmed by noise. Extensive experiments have demonstrated the superiority of our method over ten state-of-the-art (SOTA) LF SOD methods. Our code will be available at https://github.com/XucherCH/splfsam. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPLF-SAM: Self-Prompting Segment Anything Model for Light Field Salient Object Detection Xu, Qiyao Wu, Qiming Li, Xiaowei Computer Vision and Pattern Recognition Segment Anything Model (SAM) has demonstrated remarkable capabilities in solving light field salient object detection (LF SOD). However, most existing models tend to neglect the extraction of prompt information under this task. Meanwhile, traditional models ignore the analysis of frequency-domain information, which leads to small objects being overwhelmed by noise. In this paper, we put forward a novel model called self-prompting light field segment anything model (SPLF-SAM), equipped with unified multi-scale feature embedding block (UMFEB) and a multi-scale adaptive filtering adapter (MAFA). UMFEB is capable of identifying multiple objects of varying sizes, while MAFA, by learning frequency features, effectively prevents small objects from being overwhelmed by noise. Extensive experiments have demonstrated the superiority of our method over ten state-of-the-art (SOTA) LF SOD methods. Our code will be available at https://github.com/XucherCH/splfsam. |
| title | SPLF-SAM: Self-Prompting Segment Anything Model for Light Field Salient Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.19746 |