Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- referring-image-segmentation
+- vision-language
+- multimodal
+- cross-modal-reasoning
+- graph-neural-network
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>🚀 CMIRNet: Cross-Modal Interactive Reasoning Network for Referring Image Segmentation</h1>
+
+  <p>
+    <b>Mingzhu Xu</b><sup>1</sup>&nbsp;
+    <b>Tianxiang Xiao</b><sup>1</sup>&nbsp;
+    <b>Yutong Liu</b><sup>1</sup>&nbsp;
+    <b>Haoyu Tang</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
+    <b>Liqiang Nie</b><sup>1</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>Affiliation (Please update if needed)
+  </p>
+</div>
+
+These are the official implementation details and pre-trained models for **CMIRNet**, a Cross-Modal Interactive Reasoning Network designed for Referring Image Segmentation (RIS).
+
+🔗 **Paper:** IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024  
+🔗 **Task:** Referring Image Segmentation (RIS)  
+🔗 **Framework:** PyTorch  
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**CMIRNet** (Cross-Modal Interactive Reasoning Network)
+
+---
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Vision-Language / Multimodal Learning  
+- **Core Task:** Referring Image Segmentation (RIS)  
+- **Applicable Scenarios:**
+  - Language-guided object segmentation  
+  - Cross-modal reasoning  
+  - Vision-language alignment  
+  - Scene understanding with textual queries  
+
+---
+
+### 3. Project Introduction
+
+Referring Image Segmentation (RIS) aims to segment target objects in an image based on natural language descriptions. The key challenge lies in **fine-grained cross-modal alignment** and **complex reasoning between visual and linguistic modalities**.
+
+**CMIRNet** proposes a Cross-Modal Interactive Reasoning framework, which:
+
+- Introduces interactive reasoning mechanisms between visual and textual features  
+- Enhances semantic alignment via multi-stage cross-modal fusion  
+- Incorporates graph-based reasoning to capture complex relationships  
+- Improves robustness under ambiguous or complex referring expressions  
+
+---
+
+### 4. Training Data Source
+
+The model is trained and evaluated on:
+
+- RefCOCO  
+- RefCOCO+  
+- RefCOCOg  
+- RefCLEF  
+
+Image data is based on:
+
+- MS COCO 2014 Train Set (83K images)
+
+---
+
+## 🚀 Usage & Basic Inference
+
+### Step 1: Prepare Pre-trained Weights
+
+Download backbone weights:
+
+- ResNet-50  
+- ResNet-101  
+- Swin-Transformer-Base  
+- Swin-Transformer-Large  
+
+---
+
+### Step 2: Dataset Preparation
+
+1. Download COCO 2014 training images  
+2. Extract to:
+
+```
+./data/images/
+```
+
+3. Download referring datasets:
+
+```
+https://github.com/lichengunc/refer
+```
+
+---
+
+### Step 3: Training
+
+#### ResNet-based Training
+```bash
+python train_resnet.py --model_id cmirnet_refcoco_res --device cuda:0
+
+python train_resnet.py --model_id cmirnet_refcocop_res --device cuda:0 --dataset refcoco+
+
+python train_resnet.py --model_id cmirnet_refcocog_res --device cuda:0 --dataset refcocog --splitBy umd
+```
+
+#### Swin-Transformer-based Training
+```bash
+python train_swin.py --model_id cmirnet_refcoco_swin --device cuda:0
+
+python train_swin.py --model_id cmirnet_refcocop_swin --device cuda:0 --dataset refcoco+
+
+python train_swin.py --model_id cmirnet_refcocog_swin --device cuda:0 --dataset refcocog --splitBy umd
+```
+
+---
+
+### Step 4: Testing / Inference
+
+#### ResNet-based Testing
+```bash
+python test_resnet.py --device cuda:0 --resume path/to/weights
+
+python test_resnet.py --device cuda:0 --resume path/to/weights --dataset refcoco+
+
+python test_resnet.py --device cuda:0 --resume path/to/weights --dataset refcocog --splitBy umd
+```
+
+#### Swin-Transformer-based Testing
+```bash
+python test_swin.py --device cuda:0 --resume path/to/weights --window12
+
+python test_swin.py --device cuda:0 --resume path/to/weights --dataset refcoco+ --window12
+
+python test_swin.py --device cuda:0 --resume path/to/weights --dataset refcocog --splitBy umd --window12
+```
+
+---
+
+## ⚠️ Limitations & Notes
+
+- For academic research use only  
+- Performance depends on dataset quality and referring expression clarity  
+- May degrade under:
+  - ambiguous language  
+  - complex scenes  
+  - domain shift  
+- Requires substantial GPU resources for training  
+
+---
+
+## 📝⭐️ Citation
+
+```bibtex
+@ARTICLE{CMIRNet,
+  author={Xu, Mingzhu and Xiao, Tianxiang and Liu, Yutong and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang},
+  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
+  title={CMIRNet: Cross-Modal Interactive Reasoning Network for Referring Image Segmentation}, 
+  year={2024},
+  pages={1-1},
+  keywords={Referring Image Segmentation; Vision-Language; Cross Modal Reasoning; Graph Neural Network},
+  doi={10.1109/TCSVT.2024.3508752}
+}
+```
+
+---
+
+## ⭐ Acknowledgement
+
+This work builds upon advances in:
+
+- Vision-language modeling  
+- Transformer architectures  
+- Graph neural networks  
+
+---
+
+## 📬 Contact
+
+For questions or collaboration, please contact the corresponding author.