--- license: apache-2.0 pipeline_tag: image-segmentation tags: - infrared-small-target-detection - multimodal - vision-language - pytorch ---

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

Tongtong Wang1  Mingzhu Xu1✉  Chenglong Yu1  Jing Wang1  Xiaohui Lin1  Weili Guan2

1School of Software, Shandong University
2Harbin Institute of Technology, Shenzhen
✉Corresponding author

ACM MM 2026 GitHub

## 📖 Model Description 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. | Dataset | Checkpoint | | :--------: | :----------------------------------------------------------: | | IRSTD-1K | [`ADGNet_mIoU_72.38_IRSTD-1K.pth.tar`](https://huggingface.co/iLearn-Lab/MM26-ADGNet/blob/main/ADGNet_mIoU_72.38_IRSTD-1K.pth.tar) | | NUDT-SIRST | [`ADGNet_mIoU_95.53_NUDT-SIRST.pth.tar`](https://huggingface.co/iLearn-Lab/MM26-ADGNet/blob/main/ADGNet_mIoU_95.53_NUDT-SIRST.pth.tar) | | SIRST | [`ADGNet_mIoU_83.08_SIRST.pth.tar`](https://huggingface.co/iLearn-Lab/MM26-ADGNet/blob/main/ADGNet_mIoU_83.08_SIRST.pth.tar) | --- ## 🚀 Usage These checkpoints are designed to be used with the official ADGNet implementation: ```text https://github.com/iLearn-Lab/MM26-ADGNet ``` ### 1. Clone the Official Repository ```bash git clone https://github.com/iLearn-Lab/MM26-ADGNet.git cd MM26-ADGNet ``` ### 2. Prepare the Checkpoints Place the downloaded checkpoints in: ```text MM26-ADGNet/ └── SOTA_pth/ ├── ADGNet_mIoU_72.38_IRSTD-1K.pth.tar ├── ADGNet_mIoU_95.53_NUDT-SIRST.pth.tar └── ADGNet_mIoU_83.08_SIRST.pth.tar ``` ### 3. Prepare the CLIP Text Encoder ADGNet uses the pretrained **CLIP ViT-B/16** model: ```bash git clone https://huggingface.co/openai/clip-vit-base-patch16 ``` Update the local CLIP model path in the corresponding project configuration or source file before inference. ### 4. Run Evaluation Example evaluation on IRSTD-1K: ```bash python train.py \ --trainset "IRSTD-1K" \ --testset "IRSTD-1K" \ --dataset_dir "./datasets" \ --mode test \ --ckpt "./SOTA_pth/ADGNet_mIoU_72.38_IRSTD-1K.pth.tar" ``` 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: - **AITIR Text Annotations:** [`Download`](https://huggingface.co/datasets/iLearn-Lab/MM26-ADGNet-AITIR-Text) After downloading the original datasets and text annotations, organize them according to the official ADGNet repository: ```text datasets/ ├── IRSTD-1K/ │ ├── images/ │ ├── masks/ │ ├── img_idx/ │ └── text/ ├── NUDT-SIRST/ │ ├── images/ │ ├── masks/ │ ├── img_idx/ │ └── text/ └── SIRST/ ├── images/ ├── masks/ ├── img_idx/ └── text/ ``` --- ## 🎯 Intended Use The released checkpoints are intended for: - Academic research on infrared small target detection - Reproduction of the results reported in the ADGNet paper - Evaluation on IRSTD-1K, NUDT-SIRST, and SIRST - Research on multimodal and text-guided infrared image segmentation - Comparison with other infrared small target detection methods --- ## ⚠️ Limitations - The model requires both infrared images and corresponding textual prompts. - Detection performance may vary when applied to datasets or scenes that differ substantially from the training distribution. - The released checkpoints are designed for the dataset splits and evaluation settings used in the paper. - The model depends on the pretrained CLIP ViT-B/16 text encoder. - The original infrared datasets are subject to their respective licenses and terms of use. --- ## 🔗 Related Resources - **GitHub Repository:** [iLearn-Lab/MM26-ADGNet](https://github.com/iLearn-Lab/MM26-ADGNet) - **Paper:** [`ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection`](https://huggingface.co/papers/2609.00853) - **Text Annotations:** [`AITIR Text Annotations`](https://huggingface.co/datasets/iLearn-Lab/MM26-ADGNet-AITIR-Text) --- ## 📚 Citation If you find ADGNet or the released checkpoints useful in your research, please consider citing our paper: ```bibtex @inproceedings{wang2026adgnet, title = {ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection}, author = {Wang, Tongtong and Xu, Mingzhu and Yu, Chenglong and Wang, Jing and Lin, Xiaohui and Guan, Weili}, booktitle = {Proceedings of the ACM International Conference on Multimedia}, year = {2026}, } ``` Please also consider checking out and citing our other related work: ```bibtex @inproceedings{yu2026dgnet, title = {DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection}, author = {Yu, Chenglong and Xu, Mingzhu and Wang, Jing and Wang, Tongtong and Miao, Pingping and Nie, Liqiang}, booktitle = {Proceedings of the ACM International Conference on Multimedia}, year = {2026}, } @article{11017756, author = {Xu, Mingzhu and Yu, Chenglong and Li, Zexuan and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, title = {HDNet: A Hybrid Domain Network With Multiscale High-Frequency Information Enhancement for Infrared Small-Target Detection}, year = {2025}, volume = {63}, pages = {1--15}, doi = {10.1109/TGRS.2025.3574962}, } ``` ---