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README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- composed-image-retrieval
+- vision-language
+- multimodal
+- noisy-correspondence
+- blip-2
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>☁️ Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval</h1>
+
+  <p>
+    <b>Zhiheng Fu</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
+    <b>Qianyun Yang</b><sup>1</sup>&nbsp;
+    <b>Shiqi Zhang</b><sup>1</sup>&nbsp;
+    <b>Zhiwei Chen</b><sup>1</sup>&nbsp;
+    <b>Zixu Li</b><sup>1</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>School of Software, Shandong University
+  </p>
+</div>
+
+These are the official pre-trained model weights and configuration files for **Air-Know**, a robust framework designed for Composed Image Retrieval (CIR) under Noisy Correspondence Learning (NCL) settings.
+
+🔗 **Paper:** [Accepted by CVPR 2026] 
+🔗 **GitHub Repository:** [ZhihFu/Air-Know](https://github.com/ZhihFu/Air-Know)
+🔗 **Project Website:** [Air-Know Webpage](https://zhihfu.github.io/Air-Know.github.io/)
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**Air-Know** (Arbiter-Calibrated Knowledge-Internalizing Robust Network) Checkpoints.
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Composed Image Retrieval (CIR) / Noisy Correspondence Learning / Vision-Language
+- **Applicable Tasks:** Robust multimodal retrieval that effectively mitigates the impact of Noisy Triplet Correspondence (NTC) in training data, while still maintaining highly competitive performance in traditional fully-supervised (0% noise) environments.
+
+### 3. Project Introduction
+**Air-Know** is built upon the BLIP-2/LAVIS framework and tackles the noisy correspondence problem in CIR through three primary modules:
+- ⚖️ **External Prior Arbitration:** Leverages an offline multimodal expert to generate reliable arbitration priors, bypassing the often-unreliable "small-loss hypothesis".
+- 🧠 **Expert-Knowledge Internalization:** Transfers these priors into a lightweight proxy network to structurally prevent the memorization of ambiguous partial matches.
+- 🔄 **Dual-Stream Reconciliation:** Dynamically integrates the internalized knowledge to provide robust online feedback, guiding the final representation learning.
+
+### 4. Training Data Source
+The model was primarily trained and evaluated on standard CIR datasets under various simulated noise ratios (e.g., 0.0, 0.2, 0.5, 0.8):
+- **FashionIQ** (Fashion Domain)
+- **CIRR** (Open Domain)
+
+---
+
+## 🚀 Usage & Basic Inference
+
+These weights are designed to be used directly with the official Air-Know GitHub repository.
+
+### Step 1: Prepare the Environment
+Clone the GitHub repository and install dependencies (evaluated on Python 3.8.10 and PyTorch 2.1.0 with CUDA 12.1+):
+```bash
+git clone [https://github.com/ZhihFu/Air-Know](https://github.com/ZhihFu/Air-Know)
+cd Air-Know
+conda create -n airknow python=3.8 -y
+conda activate airknow
+
+# Install PyTorch
+pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121)
+
+# Install core dependencies
+pip install scikit-learn==1.3.2 transformers==4.25.0 salesforce-lavis==1.0.2 timm==0.9.16
+```
+
+### Step 2: Download Model Weights & Data
+Download the checkpoint folders (e.g., `cirr_noise0.8` or `fashioniq_noise0.8`) from this Hugging Face repository and place them in your local `checkpoints/` directory. 
+
+Ensure you also download and structure the base dataset images (CIRR and FashionIQ) as specified in the [GitHub repo's Data Preparation section](https://github.com/ZhihFu/Air-Know).
+
+### Step 3: Run Testing / Inference
+To generate prediction files on the CIRR dataset for submission to the CIRR Evaluation Server using the downloaded checkpoint, run:
+```bash
+python src/cirr_test_submission.py checkpoints/cirr_noise0.8/
+```
+*(The script will automatically output a `.json` file based on the best checkpoint in the specified folder).*
+
+To train the model under specific noise ratios (e.g., `0.8`), you can run:
+```bash
+python train_BLIP2.py \
+    --dataset cirr \
+    --cirr_path "/path/to/CIRR/" \
+    --model_dir "./checkpoints/cirr_noise0.8" \
+    --noise_ratio 0.8 \
+    --batch_size 256 \
+    --num_epochs 20 \
+    --lr 2e-5
+```
+
+---
+
+## ⚠️ Limitations & Notes
+
+**Disclaimer:** This framework and its pre-trained weights are strictly intended for **academic research purposes**.
+- The model requires access to the original source datasets (CIRR, FashionIQ) for full evaluation. Users must comply with the original licenses of those respective datasets.
+- The `noise_ratio` parameter is a simulated interference during training; performance in wild, unstructured noisy environments may vary.
+
+---
+
+## 📝⭐️ Citation
+
+If you find our work or these model weights useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repo and citing our paper:
+
+```bibtex
+@InProceedings{Air-Know,
+    title={Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval},
+    author={Fu, Zhiheng and Hu, Yupeng and Qianyun Yang and Shiqi Zhang and Chen, Zhiwei and Li, Zixu},
+    booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
+    year = {2026}
+}
+```