# SPLF-SAM: Self-Prompting Segment Anything Model for Light Field Salient Object Detection

arXiv: https://arxiv.org/abs/2508.19746 · HTML: https://mlsanity.com/paper/2508.19746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Siamese Network for RGB-D Salient Object Detection and Beyond](https://mlsanity.com/paper/2008.12134/) (2020)
- [Uncertainty Inspired RGB-D Saliency Detection](https://mlsanity.com/paper/2009.03075/) (2020)
- [Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection](https://mlsanity.com/paper/2408.04326/) (2024)
- [Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance](https://mlsanity.com/paper/2110.00698/) (2021)
- [Light Field Saliency Detection with Deep Convolutional Networks](https://mlsanity.com/paper/1906.08331/) (2019)
- [Learning Synergistic Attention for Light Field Salient Object Detection](https://mlsanity.com/paper/2104.13916/) (2021)
- [A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network](https://mlsanity.com/paper/2401.11914/) (2024)

## Abstract (arXiv)

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.