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README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- image-text-retrieval
+- vision-language
+- multimodal
+- redundancy-mitigation
+- deep-hashing
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>🚀 MEET: Redundancy Mitigation: Towards Accurate and Efficient Image-Text Retrieval</h1>
+
+  <p>
+    <b>Kun Wang</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1</sup>&nbsp;
+    <b>Hao Liu</b><sup>1</sup>&nbsp;
+    <b>Lirong Jie</b><sup>1</sup>&nbsp;
+    <b>Liqiang Nie</b><sup>2</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>School of Software, Shandong University, Jinan, China<br>
+    <sup>2</sup>School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
+  </p>
+</div>
+
+These are the official implementation, pre-trained model weights, and configuration files for **MEET**, a novel framework explicitly designed to address semantic and relationship redundancy in Image-Text Retrieval (ITR).
+
+🔗 **Paper:** [Accepted by TCSVT 2025](https://ieeexplore.ieee.org/document/11299108)
+🔗 **GitHub Repository:** [iLearn-Lab/TCSVT25-MEET](https://github.com/iLearn-Lab/TCSVT25-MEET)
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**MEET** (iMage-text retrieval rEdundancy miTigation)
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Image-Text Retrieval (ITR) / Vision-Language / Multimodal Learning
+- **Applicable Tasks:** Accurate and efficient cross-modal retrieval. It specifically addresses redundancy by mitigating semantic redundancy within unimodal representations and relationship redundancy in cross-modal alignments.
+
+### 3. Project Introduction
+Existing Image-Text Retrieval methods often suffer from a fundamental yet overlooked challenge: redundancy. **MEET** introduces an iMage-text retrieval rEdundancy miTigation framework to explicitly analyze and address the ITR problem from a redundancy perspective. This approach helps the model effectively produce compact yet highly discriminative representations for accurate and efficient retrieval.
+
+> 💡 **Method Highlight:** MEET mitigates semantic redundancy by repurposing deep hashing and quantization, and progressively refines the cross-modal alignment space by filtering misleading negative samples and adaptively reweighting informative pairs. It supports end-to-end model training, diverse feature encoders, and unified optimization.
+
+### 4. Training Data Source
+The model is evaluated using features from Bi-GRU and BERT on standard ITR datasets:
+- **MSCOCO** (1K and 5K splits)
+- **Flickr30K**
+*(Splits produced by HREM)*
+
+---
+
+## 🚀 Usage & Basic Inference
+
+### Step 1: Prepare the Environment
+Clone the GitHub repository and ensure you have the required dependencies (evaluated on Python >= 3.8 and PyTorch >= 1.7.0):
+```bash
+git clone https://github.com/iLearn-Lab/TCSVT25-MEET.git
+cd MEET
+```
+
+pip install torchvision>=0.8.0 transformers>=2.1.1 opencv-python tensorboard
+
+
+### Step 2: Download Model Weights & Data
+1. **Pre-trained Checkpoints:** Download the model checkpoints  and place them in your designated `LOGGER_PATH`.
+2. **Language Models & Features:**
+   - Obtain pretrained files for [BERT-base](https://huggingface.co/bert-base-uncased).
+   - Obtain pretrained VSE model checkpoints (e.g., [ESA](https://github.com/KevinLight831/ESA) as an example).
+3. **Datasets:** Structure the MSCOCO and Flickr30K datasets as outlined in the data tree structure (e.g., `coco_precomp`, `f30k_precomp`, `vocab`, `VSE`).
+
+### Step 3: Run Training & Evaluation
+
+**Evaluation (Eval):**
+Depending on the text features you are using, open the corresponding script (`at/lib/test.py` for BiGRU, `at_bert/lib/test.py` for BERT) and modify the `RUN_PATH`.
+
+#### For MSCOCO 1K 5-fold splits, first generate the folds:
+python scripts/make_coco_1k_folds.py
+
+#### Run testing (ensure MODEL_PATH is set to the correct VSE weights)
+PYTHONPATH=. python -m lib.test
+
+
+**Training from Scratch:**
+Make sure to specify the dataset name (`coco_precomp` or `f30k_precomp`) after the `--data_name` flag:
+
+PYTHONPATH=. python hq_train.py --num_epochs 12 --batch_size 128 --workers 8 --H 64 --M 8 --K 8 --data_name <dataset_name>
+
+
+---
+
+## ⚠️ Limitations & Notes
+
+**Disclaimer:** This framework and its pre-trained weights are intended for **academic research purposes only**.
+- The model requires access to the original source datasets (MSCOCO, Flickr30K) for full evaluation. 
+- While designed for redundancy mitigation, the performance may still fluctuate based on extreme domain shifts not covered by the training distribution.
+
+
+
+---
+
+## ⚠️ Acknowledgements & Contact
+
+- **Acknowledgement:** Thanks to the [HREM](https://github.com/crossmodalgroup/hrem) open-source community for strong baselines and tooling. Thanks to all collaborators and contributors of this project.
+- **Contact:** If you have any questions, feel free to contact me at `khylon.kun.wang@gmail.com`.
+
+---
+
+## 📝⭐️ Citation
+
+If you find our work or this repository useful in your research, please consider citing our paper:
+
+
+@article{wang2025redundancy,
+  title={Redundancy Mitigation: Towards Accurate and Efficient Image-Text Retrieval},
+  author={Wang, Kun and Hu, Yupeng and Liu, Hao and Jie, Lirong and Nie, Liqiang},
+  journal={IEEE Transactions on Circuits and Systems for Video Technology},
+  year={2025},
+  publisher={IEEE}
+}

f30k_H64_bert.tar

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f30k_H64_bigru.tar

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