LFSamba：将SAM与Mamba结合用于光场显著性目标检测
LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection
AI总结：
提出LFSamba模型用于光场显著性目标检测，利用SAM提取特征、Mamba建模切片间与跨模态关系以获取深度线索并相互增强，同时构建首个涂鸦监督基线。
摘要：
光场相机可利用捕获的含丰富空间几何信息的多聚焦图像重建3D场景，增强立体摄影、虚拟现实和机器人视觉应用。本文提出多聚焦光场图像的最先进显著性目标检测模型LFSamba，强调四个主要见解：(a) 高效特征提取，使用SAM提取模态感知判别特征；(b) 切片间关系建模，利用Mamba捕获跨多个聚焦切片的长距离依赖，从而提取隐式深度线索；(c) 跨模态关系建模，利用Mamba整合全聚焦和多聚焦图像，实现相互增强；(d) 弱监督学习能力，从现有像素级掩码数据集开发涂鸦标注数据集，建立首个光场显著性目标检测的涂鸦监督基线。https://github.com/liuzywen/LFScribble
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
