LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915013006983168 |
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| author | Liu, Zhengyi Wang, Longzhen Fang, Xianyong Tu, Zhengzheng Wang, Linbo |
| author_facet | Liu, Zhengyi Wang, Longzhen Fang, Xianyong Tu, Zhengzheng Wang, Linbo |
| contents | A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection Liu, Zhengyi Wang, Longzhen Fang, Xianyong Tu, Zhengzheng Wang, Linbo Computer Vision and Pattern Recognition A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble |
| title | LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.06652 |