SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection

• URL: http://arxiv.org/abs/2510.03689v1
• Date: Sat, 04 Oct 2025 06:02:12 GMT
• Title: SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection
• Authors: Zhengyi Liu, Xinrui Wang, Xianyong Fang, Zhengzheng Tu, Linbo Wang,
• Abstract summary: RGB-T salient object detection (SOD) aims to segment attractive objects by combining RGB and thermal infrared images.<n>We propose a model called textitSAMSOD, which utilizes unimodal supervision to enhance the learning of non-dominant modality.
• Score: 15.774524474470233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RGB-T salient object detection (SOD) aims to segment attractive objects by combining RGB and thermal infrared images. To enhance performance, the Segment Anything Model has been fine-tuned for this task. However, the imbalance convergence of two modalities and significant gradient difference between high- and low- activations are ignored, thereby leaving room for further performance enhancement. In this paper, we propose a model called \textit{SAMSOD}, which utilizes unimodal supervision to enhance the learning of non-dominant modality and employs gradient deconfliction to reduce the impact of conflicting gradients on model convergence. The method also leverages two decoupled adapters to separately mask high- and low-activation neurons, emphasizing foreground objects by enhancing background learning. Fundamental experiments on RGB-T SOD benchmark datasets and generalizability experiments on scribble supervised RGB-T SOD, fully supervised RGB-D SOD datasets and full-supervised RGB-D rail surface defect detection all demonstrate the effectiveness of our proposed method.

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