# LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection

arXiv: https://arxiv.org/abs/2411.06652 · HTML: https://mlsanity.com/paper/2411.06652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020)
- [Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence](https://mlsanity.com/paper/2012.04404/) (2020)
- [Multi-source weak supervision for saliency detection](https://mlsanity.com/paper/1904.00566/) (2019)
- [Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance](https://mlsanity.com/paper/2110.00698/) (2021)
- [Scribble-Supervised RGB-T Salient Object Detection](https://mlsanity.com/paper/2303.09733/) (2023)
- [Learning Synergistic Attention for Light Field Salient Object Detection](https://mlsanity.com/paper/2104.13916/) (2021)
- [Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection](https://mlsanity.com/paper/2204.13456/) (2022)
- [Guided Focal Stack Refinement Network for Light Field Salient Object Detection](https://mlsanity.com/paper/2305.05260/) (2023)

## Built on by (most cited first, 2 of 2)

- [LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection](https://mlsanity.com/paper/2503.00747/) (2025)
- [SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception](https://mlsanity.com/paper/2505.16304/) (2025)

## Abstract (arXiv)

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