提出SPLF-SAM模型，提升光场显著目标检测中小物体的识别能力
SPLF-SAM: Self-Prompting Segment Anything Model for Light Field Salient Object Detection
- 自适应提示机制结合多尺度特征提取，增强小目标感知
- 在10个SOTA方法中达到最优性能，尤其在小目标上提升明显
- 适合关注光场图像中微小目标检测的研究者与工程师
光场显著目标检测（LF SOD）中，现有模型普遍忽视提示信息的利用，且未充分分析频域特征，导致小目标易被噪声淹没。本文提出自提示光场分割任意模型（SPLF-SAM），包含统一多尺度特征嵌入模块（UMFEB）和多尺度自适应滤波适配器（MAFA）。UMFEB可有效识别不同尺寸的目标，MAFA通过学习频域特征，显著抑制噪声对小目标的干扰。大量实验表明，该方法在10个主流基准上优于当前最先进（SOTA）的LF SOD方法，尤其在小目标检测任务中表现突出。代码将开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。