(ICASSP 2026) HINT: Composed Image Retrieval with Dual-Path Compositional Contextualized Network (Model Weights)
1School of Software, Shandong University
✉ Corresponding author
This repository hosts the official pre-trained checkpoints for **HINT**, a novel framework designed to tackle the neglect of contextual information and the absence of discrepancy-amplification mechanisms in Composed Image Retrieval (CIR).
---
## 📌 Model Information
### 1. Model Name
**HINT** (dual-patH composItional coNtextualized neTwork) Checkpoints.
### 2. Task Type & Applicable Tasks
- **Task Type:** Composed Image Retrieval (CIR) / Vision-Language Retrieval.
- **Applicable Tasks:** Retrieving target images based on a reference image and a modification text.
### 3. Project Introduction
Existing Composed Image Retrieval (CIR) methods often suffer from the neglect of contextual information in discriminating matching samples , struggling to understand complex modifications and implicit dependencies in real-world scenarios. HINT effectively addresses this through:
- 🧩 Dual Context Extraction (DCE): Extracts both intra-modal context and cross-modal context, enhancing joint semantic representation by integrating multimodal contextual information.
- 📏 Quantification of Contextual Relevance (QCR): Measures the relevance between cross-modal contextual information and the target image semantics, enabling the quantification of the implicit dependencies.
- ⚖️ Dual-Path Consistency Constraints (DPCC): Optimizes the training process by constraining representation consistency, ensuring the stable enhancement of similarity for matching instances while lowering it for non-matching ones.
Based on the BLIP-2 architecture , HINT achieves State-of-the-Art (SOTA) retrieval performance across both open-domain and fashion-domain benchmarks.
### 4. Training Data Source & Hosted Weights
The models were trained on the **FashionIQ** and **CIRR** datasets . This Hugging Face repository provides the corresponding `.pt` checkpoint files organized by dataset:
* `fashioniq.pt` (Trained on FashionIQ)
* `cirr.pt` (Trained on CIRR)
---
## 🚀 Usage & Basic Inference
These weights are designed to be evaluated seamlessly using the official [HINT GitHub repository](https://github.com/iLearn-Lab/ICASSP26-HINT).
### Step 1: Prepare the Environment
Clone the GitHub repository and install dependencies:
```bash
git clone https://github.com/iLearn-Lab/ICASSP26-HINT
cd ICASSP26-HINT
conda create -n hint python=3.8 -y
conda activate hint
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
pip install open-clip-torch==2.24.0 scikit-learn==1.3.2 transformers==4.25.0 salesforce-lavis==1.0.2 timm==0.9.16
```
### Step 2: Download Model Weights
Download the specific `.pt` files you wish to evaluate from this Hugging Face repository. Place them into a `checkpoints/` directory within your cloned GitHub repo. For example, to evaluate the CIRR model:
```text
ICASSP26-HINT/
└── checkpoints/
└── cirr.pt <-- (Rename to best_model.pt if required by your specific test script)
```
### Step 3: Run Testing / Evaluation
To generate prediction files on the CIRR dataset for the [CIRR Evaluation Server](https://cirr.cecs.anu.edu.au/), point the test script to the directory containing your downloaded checkpoint:
```bash
python src/cirr_test_submission.py checkpoints/
```
*(The script will automatically output `.json` files based on the checkpoint for online evaluation.)*
---
## ⚠️ Limitations & Notes
- **Hardware Requirements:** Because HINT is built upon the powerful BLIP-2 architecture, inference and further fine-tuning require GPUs with sufficient memory (e.g., NVIDIA A40 48G / V100 32G is recommended).
- **Intended Use:** These weights are provided for academic research and to facilitate reproducibility of the ICASSP 2026 paper.
---
## 📝⭐️ Citation
If you find our work, code, or these model weights useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repository and citing our paper:
```bibtex
@inproceedings{HINT2026,
title={HINT: COMPOSED IMAGE RETRIEVAL WITH DUAL-PATH COMPOSITIONAL CONTEXTUALIZED NETWORK},
author={Zhang, Mingyu and Li, Zixu and Chen, Zhiwei and Fu, Zhiheng and Zhu, Xiaowei and Nie, Jiajia and Wei, Yinwei and Hu, Yupeng},
booktitle={Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2026}
}
```