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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
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>⚓ TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval</h1>
+
+  <p>
+    <b>Zixu Li</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
+    <b>Zhiheng Fu</b><sup>1</sup>&nbsp;
+    <b>Zhiwei Chen</b><sup>1</sup>&nbsp;
+    <b>Yongqi Li</b><sup>2</sup>&nbsp;
+    <b>Liqiang Nie</b><sup>3</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>Shandong University&nbsp;
+    <sup>2</sup>Hong Kong Polytechnic University&nbsp;&nbsp;
+    <sup>3</sup>Harbin Institute of Technology (Shenzhen)
+  </p>
+</div>
+
+These are the official model and data resources for **TEMA** (Text-oriented Entity Mapping Architecture), the first Composed Image Retrieval (CIR) framework designed explicitly for multi-modification scenarios. 
+
+🔗 **Paper:** [Accepted by ACL 2026] 
+🔗 **GitHub Repository:** [lee-zixu/ACL26-TEMA](https://github.com/lee-zixu/ACL26-TEMA)
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**TEMA** (Text-oriented Entity Mapping Architecture) Checkpoints.
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Composed Image Retrieval (CIR) / Vision-Language / Multimodal Alignment
+- **Applicable Tasks:** Retrieving target images based on a reference image and complex Multi-Modification Texts (MMT), seamlessly accommodating both simple and complex modifications.
+
+### 3. Project Introduction
+Prevailing CIR setups rely on simple modification texts, inducing two critical limitations in practical applications: **Insufficient Entity Coverage** and **Clause-Entity Misalignment**. 
+
+**TEMA** brings CIR closer to real-world use cases by introducing:
+- 🧠 **MMT Parsing Assistant (PA):** Utilizes an LLM-based text summarizer and a Consistency Detector during training to enhance the exposure and coverage of modified entities.
+- 🔗 **MMT-oriented Entity Mapping (EM):** Introduces learnable queries to consolidate multiple clauses of the same entity on the text side and align them with corresponding visual entities on the image side.
+
+### 4. Training Data Source
+The model is evaluated and trained using our newly proposed instruction-rich multi-modification datasets: 
+- **M-FashionIQ** (Fashion domain)
+- **M-CIRR** (Open domain)
+
+These datasets replace short, simplistic texts with Multi-Modification Texts (MMT) generated by MLLM and verified by human annotators.
+
+---
+
+## 🚀 Usage & Basic Inference
+
+These weights and codes are designed to be used with the official TEMA GitHub repository.
+
+### Step 1: Prepare the Environment
+Clone the GitHub repository and install the required dependencies (evaluated on Python 3.10.8 and PyTorch 2.5.1):
+```bash
+git clone [https://github.com/lee-zixu/ACL26-TEMA](https://github.com/lee-zixu/ACL26-TEMA)
+cd TEMA
+conda create -n tema python=3.10.8 -y
+conda activate tema
+
+# Install PyTorch
+pip install torch==2.5.1 torchvision torchaudio --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121)
+
+# Install core dependencies
+pip install transformers==4.25.0
+```
+
+### Step 2: Download Model & Data
+Please refer to the [GitHub Repository](https://github.com/lee-zixu/ACL26-TEMA) for detailed instructions on downloading the base image datasets (FashionIQ and CIRR) and replacing their captions with our provided `mmt_captions` to construct the **M-FashionIQ** and **M-CIRR** datasets. 
+
+Ensure your folder structure matches the requirements in the official codebase.
+
+### Step 3: Run Training / Inference
+Once the environment and datasets are prepared, you can start the training or evaluation process:
+```bash
+python3 train.py
+```
+
+---
+
+## ⚠️ Limitations & Notes
+
+**Disclaimer:** This framework and the constructed M-FashionIQ/M-CIRR datasets are intended for **academic research and multimodal evaluation**. 
+- The datasets build upon existing public datasets (FashionIQ and CIRR); users must also comply with the original licenses of those datasets.
+- The model's performance relies heavily on the quality of instruction parsing, and real-world multi-modification accuracy may vary based on domain-specific data.
+
+---
+
+## 📝⭐️ Citation
+
+If you find our paper, the M-FashionIQ/M-CIRR datasets, or this codebase useful in your research, please consider leaving a **Star** ⭐️ on our GitHub repo and citing our work:
+
+```bibtex
+@inproceedings{TEMA,
+  title={TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval},
+  author={Li, Zixu and Hu, Yupeng and Fu, Zhiheng and Chen, Zhiwei and Li, Yongqi and Nie, Liqiang},
+  booktitle={Proceedings of the Association for Computational Linguistics (ACL)},
+  year={2026}
+}
+```