This repository provides the official model checkpoints for ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection, accepted by ACM Multimedia 2026.
Infrared Small Target Detection aims to accurately segment weak and tiny targets from complex infrared backgrounds. Existing pure-vision methods rely mainly on pixel-level information, while existing vision-language methods commonly describe targets and backgrounds using a single textual prompt. Such a symmetric design overlooks the inherent semantic differences between sparse infrared targets and structurally complex backgrounds.
ADGNet addresses this problem through three main components:
Asymmetric Dual-text Prompt (ADP): uses an abstract, image-independent target prompt and a detailed, image-dependent background prompt.
Asymmetric Dual-Branch Interaction (ADBI): independently performs target localization and background suppression using their corresponding textual priors.
Adaptive Feature Aggregation (AFA): dynamically fuses target-enhanced and background-suppressed features for accurate segmentation.
The model uses the pretrained CLIP ViT-B/16 text encoder to extract semantic representations from the target and background prompts.
🏆 Available Checkpoints
All ADGNet checkpoints are hosted in this Hugging Face model repository.
Download the required checkpoint directly from the Files and versions section of this repository.
Replace the dataset name and checkpoint path when evaluating on NUDT-SIRST or SIRST.
📂 Dataset and Text Annotation Preparation
The original infrared images and ground-truth masks are not included in this model repository. Please obtain IRSTD-1K, NUDT-SIRST, and SIRST from their respective official sources.
The asymmetric text annotations used by ADGNet are released separately in our Hugging Face dataset repository:
