HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection

• URL: http://arxiv.org/abs/2509.18738v1
• Date: Tue, 23 Sep 2025 07:32:11 GMT
• Title: HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection
• Authors: Ruichao Hou, Xingyuan Li, Tongwei Ren, Dongming Zhou, Gangshan Wu, Jinde Cao,
• Abstract summary: We propose a novel prompt-driven segment anything model (HyPSAM) for RGB-T SOD.<n> DFNet employs dynamic convolution and multi-branch decoding to facilitate adaptive cross-modality interaction.<n>Experiments on three public datasets demonstrate that our method achieves state-of-the-art performance.
• Score: 75.406055413928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RGB-thermal salient object detection (RGB-T SOD) aims to identify prominent objects by integrating complementary information from RGB and thermal modalities. However, learning the precise boundaries and complete objects remains challenging due to the intrinsic insufficient feature fusion and the extrinsic limitations of data scarcity. In this paper, we propose a novel hybrid prompt-driven segment anything model (HyPSAM), which leverages the zero-shot generalization capabilities of the segment anything model (SAM) for RGB-T SOD. Specifically, we first propose a dynamic fusion network (DFNet) that generates high-quality initial saliency maps as visual prompts. DFNet employs dynamic convolution and multi-branch decoding to facilitate adaptive cross-modality interaction, overcoming the limitations of fixed-parameter kernels and enhancing multi-modal feature representation. Moreover, we propose a plug-and-play refinement network (P2RNet), which serves as a general optimization strategy to guide SAM in refining saliency maps by using hybrid prompts. The text prompt ensures reliable modality input, while the mask and box prompts enable precise salient object localization. Extensive experiments on three public datasets demonstrate that our method achieves state-of-the-art performance. Notably, HyPSAM has remarkable versatility, seamlessly integrating with different RGB-T SOD methods to achieve significant performance gains, thereby highlighting the potential of prompt engineering in this field. The code and results of our method are available at: https://github.com/milotic233/HyPSAM.

Related papers

• KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection [35.52055285209549]
  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.<n>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.<n>We also introduce a mutually exclusive random masking strategy to reduce reliance on RGB data and improve generalization.
  arXiv  Detail & Related papers  (2025-04-08T10:07:02Z)
• SSFam: Scribble Supervised Salient Object Detection Family [13.369217449092524]
  Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods.
  arXiv  Detail & Related papers  (2024-09-07T13:07:59Z)
• Interactive Context-Aware Network for RGB-T Salient Object Detection [7.544240329265388]
  We propose a novel network called Interactive Context-Aware Network (ICANet) ICANet contains three modules that can effectively perform the cross-modal and cross-scale fusions. Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods.
  arXiv  Detail & Related papers  (2022-11-11T10:04:36Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• RGB-D Salient Object Detection with Cross-Modality Modulation and Selection [126.4462739820643]
  We present an effective method to progressively integrate and refine the cross-modality complementarities for RGB-D salient object detection (SOD) The proposed network mainly solves two challenging issues: 1) how to effectively integrate the complementary information from RGB image and its corresponding depth map, and 2) how to adaptively select more saliency-related features.
  arXiv  Detail & Related papers  (2020-07-14T14:22:50Z)
• Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection [91.43066633305662]
  The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We implement a kind of more flexible and efficient multi-scale cross-modal feature processing.
  arXiv  Detail & Related papers  (2020-07-13T07:59:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.