ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2609.00853v1
• Date: Tue, 01 Sep 2026 07:48:22 GMT
• Title: ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection
• Abstract summary: InfRared Small Target Detection (IRSTD) is a challenging task.<n>Current multimodal methods describe both targets and backgrounds with a single textual prompt.<n>We propose a novel Asymmetric Dual-text Guided Network (ADGNet)
• Score: 21.00991696055637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: InfRared Small Target Detection (IRSTD) is a challenging task. Relying solely on pixel-level information, vision-only methods struggle to distinguish targets from clutter. Current multimodal methods typically describe both targets and backgrounds with a single textual prompt. Such an approach lacks dedicated regional guidance and ignores infrared semantic asymmetry. Consequently, it provides insufficient background suppression information and introduces severe feature optimization conflicts, overwhelming small targets with noise. To address these issues, we propose a novel Asymmetric Dual-text Guided Network (ADGNet). Specifically, accounting for the infrared semantic asymmetry, we first design the Asymmetric Dual-text Prompt (ADP), comprising an image-agnostic abstract target prompt and an image-specific detailed background prompt. To leverage these prompts, we introduce an Asymmetric Dual-Branch Interaction (ADBI) module to separately guide visual features with their respective text priors, protecting targets from noise while suppressing background clutter. Subsequently, we introduce an Adaptive Feature Aggregation (AFA) module to dynamically fuse features from the two branches. Furthermore, we construct a multimodal Asymmetric Image-Text Infrared (AITIR) dataset by providing asymmetric text annotations for three public datasets (IRSTD-1K, NUDT-SIRST, and SIRST). Extensive experiments demonstrate that ADGNet outperforms 21 state-of-the-art (SOTA) methods. Code is available at https://github.com/iLearn-Lab/MM26-ADGNet.

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