psp-dada

Qwen2-VL-2B-Instruct-SENTINEL

README

License: apache-2.0

🎊 News

[2025.07.30] 🔍 Our work has been featured and explained by 52CV, check it out here.
[2025.07.21] 📖 All code, data, and models are released!
[2025.06.26] 🎉 Our SENTINEL is accepted by ICCV 2025!

🚀 Overview

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.

🔍 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.

💡 Context matters: rich coherence drives robustness. By prioritizing context-coherent positive samples over hallucinated ones, SENTINEL significantly boosts generalization.

♻️ 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.

📊 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.

How to use

This model is a PEFT (LoRA) adapter. You first need to load the base model (Qwen/Qwen2-VL-2B-Instruct) and then load this adapter on top of it.

For the details of this model, please refer to the documentation of the GitHub repo.

📝 Citation

If you find our model/code/data/paper helpful, please consider citing our papers 📝 and starring us ⭐️！

bibtex

@inproceedings{SENTINEL_2025,  title     = {Mitigating Object Hallucinations via Sentence-Level Early Intervention},  author    = {Peng, Shangpin and Yang, Senqiao and Jiang, Li and Tian, Zhuotao},  booktitle = {Proceedings of the IEEE International Conference on Computer Vision},  pages     = {635--646},  year      = {2025}}

📧 Contact us

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.

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Model Details

Model Provider

psp-dada

Model Tree

Base

Qwen/Qwen2-VL-2B-Instruct

Adapter

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality