Update README.md

README.md

@@ -7,13 +7,94 @@ language:
 base_model:
 - llava-hf/llava-v1.6-vicuna-13b-hf
 pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- lora
 ---
 
-# Model Card for SENTINEL:<br> Mitigating Object Hallucinations via Sentence-Level Early Intervention <!-- omit in toc -->
+# Model Card for ``psp-dada/LLaVA-v1.6-Vicuna-13B-SENTINEL`` | ICCV2025 | SENTINEL:<br>Mitigating Object Hallucinations via Sentence-Level Early Intervention <!-- omit in toc -->
 
 <a href='https://arxiv.org/abs/2507.12455'>
 <img src='https://img.shields.io/badge/Paper-Arxiv-purple'></a>
 <a href='https://github.com/pspdada/SENTINEL'>
 <img src='https://img.shields.io/badge/Github-Repo-Green'></a>
+<a href='https://huggingface.co/papers/2507.12455'>
+<img src='https://img.shields.io/badge/Discussion-HF-blue'></a>
+<a href='https://github.com/pspdada/SENTINEL/blob/main/LICENSE'>
+<img src='https://img.shields.io/badge/LICENSE-Apache_2.0-yellow'></a>
 
-For the details of this model, please refer to the [documentation](https://github.com/pspdada/SENTINEL?tab=readme-ov-file#-model-weights) of the GitHub repo.
+## 🎊 News <!-- omit in toc -->
+
+- [2025.07.21] All code, data, and models are released!
+- [2025.06.26] 🎉 Our SENTINEL is accepted by **ICCV 2025**!
+
+## 🚀 Overview <!-- omit in toc -->
+
+**SENTINEL** introduces an automatic, sentence‑level early intervention strategy to prevent and mitigate object hallucinations in multimodal large language models (MLLMs). Key advantages:
+
+- **Annotation‑free**: No human labeling required.
+
+- **Model-agnostic**: Compatible with any MLLM architecture.
+
+- **Efficient**: Lightweight LoRA fine‑tuning.
+
+## 🔑 Key Features
+
+- 🧠 **Early intervention halts hallucination propagation**. We find that hallucinations of MLLMs predominantly arise in early sentences and propagate through the rest of the output. SENTINEL interrupts this chain early to maximize mitigation.
+<table align="center">
+    <p align="center">
+      <img src="https://github.com/pspdada/SENTINEL/raw/main/docs/figures/figure2.png" width="80%" />
+    </p>
+</table>
+
+- 🔍 **In-domain contextual preference learning without human labels**. SENTINEL constructs hallucinated/factual samples via detector cross-validation and builds context-aware preference data without relying on proprietary LLMs or manual annotations.
+<table align="center">
+    <p align="center">
+      <img src="https://github.com/pspdada/SENTINEL/raw/main/docs/figures/figure3.png" width="80%" />
+    </p>
+</table>
+
+- 💡 **Context matters: rich coherence drives robustness**. By prioritizing context-coherent positive samples over hallucinated ones, SENTINEL significantly boosts generalization.
+<table align="center">
+    <p align="center">
+      <img src="https://github.com/pspdada/SENTINEL/raw/main/docs/figures/figure4.png" width="80%" />
+    </p>
+</table>
+
+- ♻️ **Iterative contextual bootstrapping for diverse hallucination-free contexts**. Our pipeline dynamically grows non-hallucinated contexts and expands coverage across varied scenes, improving robustness across generations.
+<table align="center">
+    <p align="center">
+      <img src="https://github.com/pspdada/SENTINEL/raw/main/docs/figures/figure5.png" width="80%" />
+    </p>
+</table>
+
+- 📊 **State-of-the-art results across benchmarks**.
+SENTINEL achieves **up to 92% reduction** in hallucinations and outperforms prior SOTA methods across Object HalBench, AMBER, and HallusionBench, while maintaining or improving general task performance.
+<table align="center">
+    <p align="center">
+      <img src="https://github.com/pspdada/SENTINEL/raw/main/docs/figures/table1.png" width="80%" />
+    </p>
+</table>
+
+## How to use
+
+This model is a PEFT (LoRA) adapter. You first need to load the base model (`llava-hf/llava-v1.6-vicuna-13b-hf`) and then load this adapter on top of it.
+
+**For the details of this model, please refer to the [documentation](https://github.com/pspdada/SENTINEL?tab=readme-ov-file#-model-weights) of the GitHub repo.**
+
+## 📝 Citation
+
+If you find our model/code/data/paper helpful, please consider citing our papers 📝 and starring us ⭐️！
+
+```bibtex
+@article{peng2025mitigating,
+  title={Mitigating Object Hallucinations via Sentence-Level Early Intervention},
+  author={Peng, Shangpin and Yang, Senqiao and Jiang, Li and Tian, Zhuotao},
+  journal={arXiv preprint arXiv:2507.12455},
+  year={2025}
+}
+```
+
+## 📧 Contact us <!-- omit in toc -->
+
+If you have any questions, comments, or suggestions, please do not hesitate to submit an issue or PR to help advance research in this area.