README.md

---

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



<h1>ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection</h1>

<p>
  <b>Tongtong Wang</b><sup>1</sup>&nbsp;
  <b>Mingzhu Xu</b><sup>1✉</sup>&nbsp;
  <b>Chenglong Yu</b><sup>1</sup>&nbsp;
  <b>Jing Wang</b><sup>1</sup>&nbsp;
  <b>Xiaohui Lin</b><sup>1</sup>&nbsp;
  <b>Weili Guan</b><sup>2</sup>
</p>

<p>
  <sup>1</sup>School of Software, Shandong University<br>
  <sup>2</sup>Harbin Institute of Technology, Shenzhen<br>
  <sup>✉</sup>Corresponding author
</p>

<p>
  <a href="<paper-link>">
    <img src="https://img.shields.io/badge/ACM%20MM-2026-blue" alt="ACM MM 2026">

  </a>

  <a href="https://github.com/iLearn-Lab/MM26-ADGNet">

    <img src="https://img.shields.io/badge/GitHub-MM26--ADGNet-black?logo=github" alt="GitHub">

  </a>

</p>


## 📖 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`](<huggingface-text-dataset-link>)

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`](<paper-link>)
- **Text Annotations:** [`AITIR Text Annotations`](<huggingface-text-dataset-link>)

---

## 📚 Citation

If you find ADGNet or the released checkpoints useful in your research, please consider citing our paper:

```bibtex



```

Please also consider checking out and citing our other related work:

```bibtex



```

---