Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+datasets:
+- HaHaJun1101/OACIRR
+base_model:
+- Salesforce/blip2-itm-vit-g
+- Salesforce/blip2-itm-vit-g-coco
+library_name: pytorch
+tags:
+  - composed-image-retrieval
+  - object-anchored
+  - image-retrieval
+  - vision-language
+  - multimodal
+  - cvpr2026
+---
+
+# **🔍 Beyond Semantic Search: Towards Referential Anchoring in Composed Image Retrieval (CVPR 2026)**
+
+[**📖 Paper (arXiv)**](https://arxiv.org) | [**🌐 Homepage**](https://hahajun1101.github.io/OACIR/) | [**🐙 Code (GitHub)**](https://github.com/HaHaJun1101/OACIR) | [**🤗 Dataset (OACIRR)**](https://huggingface.co/datasets/HaHaJun1101/OACIRR) | <a href="#downloading-the-adafocal-weights" style="color: red;">**🛜 Download Weights Now 👇**</a>
+
+---
+
+## 🔔 News
+- **🔥 [2026-04-07]: The *AdaFocal* model checkpoints are officially released and are now available for use!**
+- **🔥 [2026-04-03]: The full Training/Evaluation code are officially released on GitHub!**
+- **🔥 [2026-03-25]: The OACIRR Benchmark is officially released on HuggingFace!**
+- **🎉 [2026-02-21]: Our paper "Beyond Semantic Search: Towards Referential Anchoring in Composed Image Retrieval" has been accepted to CVPR 2026!**
+
+---
+
+## 🤖 Model Description
+
+- **Architecture: ViT-G (EVA-CLIP) + BLIP-2 Q-Former + Context-Aware Attention Modulator (CAAM)**
+- **Task: Fine-grained Composed Image Retrieval (CIR) with Instance-level Consistency**
+- **Training Data: Exclusively trained on the [OACIRR Union Dataset](https://huggingface.co/datasets/HaHaJun1101/OACIRR)**
+
+---
+
+## ⚙️ AdaFocal Framework
+
+To address the core challenges of the OACIR task, we propose **AdaFocal**, an effective framework that dynamically modulates visual attention for precise, instance-level retrieval. Our approach augments a multimodal fusion backbone with a lightweight **Context-Aware Attention Modulator (CAAM)**, enabling a nuanced balance between instance fidelity and compositional reasoning.
+
+<p align="left">
+  <img 
+    src="https://huggingface.co/datasets/HaHaJun1101/OACIRR/resolve/main/figures/AdaFocal_framework.png" 
+    width="90%" 
+    alt="AdaFocal Framework Overview"
+  />
+</p>
+
+Specifically, **AdaFocal** employs a two-stage reasoning process: *Contextual Perception* and *Adaptive Focus*. It first perceives the query's compositional context to predict a modulation scalar (β). This learned signal then drives an Attention Activation Mechanism, which explicitly and adaptively intensifies the visual focus on the user-specified instance region (provided via bounding box) during multimodal feature fusion.
+
+By dynamically re-weighting the attention distribution, **AdaFocal** seamlessly synthesizes the anchored instance, the global visual scene, and the textual modification into a coherent representation, establishing a robust and flexible baseline for identity-preserving retrieval.
+
+---
+
+## 🚀 How to Use
+
+<a name="downloading-the-adafocal-weights"></a>
+
+### 1. Download the AdaFocal Weights
+
+You can download the checkpoints using Git LFS:
+```bash
+cd OACIR
+git lfs install
+git clone https://huggingface.co/HaHaJun1101/AdaFocal ./checkpoints
+```
+
+Alternatively, download them via the Hugging Face Python API:
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(repo_id="HaHaJun1101/AdaFocal", local_dir="OACIR/checkpoints", repo_type="model")
+```
+
+### 2. Run Evaluation via Official Codebase
+
+Once downloaded, you can directly evaluate the models using the `evaluate.sh` script provided in our GitHub codebase. Open `evaluate.sh` and set the path to your downloaded weights:
+```bash
+# Inside evaluate.sh
+DATASET="Fashion"
+MODEL_NAME="oacir_adafocal"
+MODEL_WEIGHT="./checkpoints/adafocal_scalar.pt"  # or adafocal_vector.pt
+```
+Then execute the script:
+```bash
+bash evaluate.sh
+```
+
+---
+
+## 🏆 Model Performance on OACIRR
+
+We provide two variants of the **AdaFocal** weights. You can instantly reproduce the following results using our provided `evaluate.sh` script.
+
+| Model Variant | Component Type | R<sub>ID</sub>@1 (Avg) | R@1 (Avg) | R@5 (Avg) | Overall Avg | Weights File |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| **AdaFocal (Scalar β)** | Default Configuration | 81.52 | 63.08 | 90.98 | **78.53** | [📥 `adafocal_scalar.pt`](https://huggingface.co/HaHaJun1101/AdaFocal/resolve/main/adafocal_scalar.pt) |
+| **AdaFocal (Vector β)** | Vector Ablation | 81.99 | 63.06 | 91.35 | **78.80** | [📥 `adafocal_vector.pt`](https://huggingface.co/HaHaJun1101/AdaFocal/resolve/main/adafocal_vector.pt) |
+
+*Detailed breakdowns across the 4 domains:*
+
+| Variant | <font color=#990000>Fashion</font> (R<sub>ID</sub>@1 / R@1) | <font color=#CC3300>Car</font> (R<sub>ID</sub>@1 / R@1) | <font color=#003399>Product</font> (R<sub>ID</sub>@1 / R@1) | <font color=#006633>Landmark</font> (R<sub>ID</sub>@1 / R@1) | 
+|:---|:---:|:---:|:---:|:---:|
+| **Scalar β** | 73.68 / 64.45 | 78.39 / 54.85 | 91.36 / 73.85 | 82.65 / 59.18 |
+| **Vector β** | 75.71 / 65.97 | 77.97 / 54.35 | 91.39 / 73.30 | 82.90 / 58.63 |
+
+---
+
+## ✒️ Citation
+
+If you find our dataset, models, or code useful in your research, please consider citing our paper.