# KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection

Source: [KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection](<https://www.sota2.com/research/paper/68fa77db1e209131cea7597d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:27:29.052Z

Authors: Xingyuan Li, Ruichao Hou, Tongwei Ren, Gangshan Wu

Published: 2025-04-08T10:07:02.000Z

## Abstract

Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to identify visually significant objects by leveraging both RGB and thermal modalities to enable robust performance in complex scenarios, but they often suffer from limited generalization due to the constrained diversity of available datasets and the inefficiencies in constructing multi-modal representations. In this paper, we propose a novel prompt learning-based RGB-T SOD method, named KAN-SAM, which reveals the potential of visual foundational models for RGB-T SOD tasks. Specifically, we extend Segment Anything Model 2 (SAM2) for RGB-T SOD by introducing thermal features as guiding prompts through efficient and accurate Kolmogorov-Arnold Network (KAN) adapters, which effectively enhance RGB representations and improve robustness. Furthermore, we introduce a mutually exclusive random masking strategy to reduce reliance on RGB data and improve generalization. Experimental results on benchmarks demonstrate superior performance over the state-of-the-art methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [RGB-D Video Salient Object Detection on DViSal](<https://www.sota2.com/research/sota/rgb-d-video-salient-object-detection-on-dvisal.md>): RGB-D Video Salient Object Detection; DVisal; S\_alpha: 83.5.

- [RGB-D Video Salient Object Detection on ViDSOD-100](<https://www.sota2.com/research/sota/rgb-d-video-salient-object-detection-on-vidsod-100.md>): RGB-D Video Salient Object Detection; ViDSOD-100; S\_alpha: 89.2.

- [RGB-D Video Salient Object Detection on RDVS](<https://www.sota2.com/research/sota/rgb-d-video-salient-object-detection-on-rdvs.md>): RGB-D Video Salient Object Detection; RDVS; S\_alpha: 85.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.05878>)

[PDF](<https://arxiv.org/pdf/2504.05878.pdf>)