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Code for paper: Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection

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# Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
Le Yang*, Ziwei Zheng*, Boxu Chen, Zhengyu Zhao, Chenhao Lin, Chao Shen. 
*equal contribution.

XJTU AISEC Team.

This repository contains the official code of Nullu, a method for mitigating object hallucinations in LVLMs via HalluSpace Projection.

[![License: MIT](https://img.shields.io/badge/License-MIT-g.svg)](https://opensource.org/licenses/MIT) [![Arxiv](https://img.shields.io/badge/Paper-Arxiv-red)](http://arxiv.org/abs/2412.13817)

## Overview
<div align="center">
  <img src="figs/fig_cap.gif" width="60%">
</div>

- We introduce a novel method named Nullu, which can effectively mitigate object hallucinations (OH) with **no extra inference cost**.
- Nullu edit weight by **extracting HalluSpaces** and **orthogonalizing the model weights** based on representation learning:
  - **HalluSpace Extraction:** Nullu first calculates the difference matrix of hidden features for the paired truthful and hallucinated samples and then conducts the Singular Value Decomposition (SVD), to find the main directions of the difference as the HalluSpace.
  - **Weight Projection:** Then Nullu projects the original MLP’s weights to the null space of the HalluSpace. This procedure will be repeated for a series of layers, $\{\ell\}$, in the LLM of an LVLM.

##  Getting Started
### :pushpin: Installation
Git clone our repository, creating a python environment and activate it via the following command.
```bash
    git clone https://github.com/Ziwei-Zheng/Nullu.git
    cd Nullu
    conda env create -f environment.yml
    conda activate nullu
```
#### Model Setup
Prepare the following model checkpoints:
- **LLaVA-1.5 7B model**: Download weights from [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b).
- **MiniGPT-4 (LLaMA-2 Chat 7B)**: Download pretrained weights from [this link](https://drive.google.com/file/d/11nAPjEok8eAGGEG1N2vXo3kBLCg0WgUk/view?usp=sharing). Set the path in `minigpt4/minigpt4_llama2_eval.yaml` (Line 8).
- **MiniGPT-4 corresponding LLM**: Download weights from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
- **mPLUG-Owl2 model**: Download from [MAGAer13/mplug-owl2-llama2-7b](https://huggingface.co/MAGAer13/mplug-owl2-llama2-7b).

To use a specific model, ensure the `--model_path` argument is set correctly. For LLaVA-1.5 and mPLUG-Owl2, use the path to the model weights, while for MiniGPT-4, set the path to the corresponding LLM weights.
 
Before model editing, you need to to install a specific version of transformers:
- For **LLaVA-1.5** and **MiniGPT4**:
    ```bash
    pip install transformers==4.37.2
    ```
- For **mPLUG-Owl2**:
    ```bash
    pip install transformers==4.31.0
    ```
#### Dataset
Model editing and evaluation require the MSCOCO 2014 dataset. Please download [here](https://cocodataset.org/#download).

### :hammer_and_wrench: Model editing

**1. Extract Model Activations from paired samples**

To extract activations for hallucinated and truthful samples, we use paired samples from [LURE/dataset_train](https://github.com/YiyangZhou/LURE/tree/main/dataset_train), stored in `data/LURE`.
- Check the value of the key `lure` in the `dataset_roots` within `dataset/__init__.py`. This specifies the path of the COCO 2014 dataset.
- Run the following command under the root directory:
    ```bash
    python scripts/model_run.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path /path/to/raw_model/
    ```
The corresponding `activations.pkl` file will be saved in `./output/{args.model_name}/`.
  
**2. Model Editing with Activations**

Use the activations stored in `emb_path` to edit the model. Key parameters include:
- *top_k_rank:* Specifies the number of singular vectors to select for the editing process. Only the first `top_k_rank` singular vectors are used.
- *layer_range:* Defines the range of layers to apply the editing to, defined as `np.arange(lowest_layer, highest_layer)`.

Run the following command under the root directory:
```bash
python scripts/model_edit.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path [PATH_TO_RAW_MODEL_WEIGHTS] --emb_path /path/to/activation.pkl --top_k_ranks 4 --lowest_layer 16 --highest_layer 32
```
The edited model weights will be saved in `./output/edited_model/`.

### :bookmark_tabs: Evalutaion

#### CHAIR, POPE, OPOPE
Update dataset paths in `dataset_roots` located in `dataset/__init__.py` as needed.

Following [Evaluating Object Hallucination in Large Vision-Language Models](https://arxiv.org/pdf/2305.10355), we used "Please describe this image in detail." as the prompt to query LVLM for captions of the 500 images randomly sampled from COCO 2014 Val datast. 
- *CHAIR* 
  
  Check the path of `data_path` and `caption_file_path` in `scripts/calculate_chair.py`. Then run the following command under the root directory:
    ```bash
    python scripts/model_response.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path [PATH_TO_MODEL_WEIGHTS] --dataset chair --num_samples 500 --num_beams 3 --max_length 64
    ```
    The results will be saved in `eval/chair/{os.path.basename(args.model_path)}`.

The POPE files built following [POPE: Polling-based Object Probing Evaluation for Object Hallucination](https://github.com/RUCAIBox/POPE) is stored in `data/POPE`.
- *POPE* 
  
    Check the path of `testfiles` in `dataset/POPE.py`. Then run the following command under the root directory:
    ```bash
    python scripts/model_response.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path [PATH_TO_MODEL_WEIGHTS] --dataset pope --num_samples 500 --num_beams 3 --max_length 64
    ```
    The results will be saved in `eval/opope/{os.path.basename(args.model_path)}`.
- *OPOPE* 
    
    Check the paths of `testfile` in `dataset/OPOPE.py` and `testfiles` in `scripts/calculate_opope.py`. Then run the following command under the root directory:
    ```bash
    python scripts/model_response.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path [PATH_TO_MODEL_WEIGHTS] --dataset opope --num_samples 500 --num_beams 3 --max_length 64
    ```
    The results will be saved in `eval/opope/{os.path.basename(args.model_path)}`.

#### MME
Download [MME_Benchmark_release_version.zip](https://huggingface.co/datasets/darkyarding/MME/blob/main/MME_Benchmark_release_version.zip) and [eval_tool.zip](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/blob/Evaluation/tools/eval_tool.zip). Then run the following command under the root directory:
```bash
python scripts/mme_response.py --reference_dir eval_tool/Your_Results --base_dir MME_Benchmark_release_version
```
The response file will be stored in `eval/mme/{os.path.basename(args.model_path)}`. To calculate the score, run:
```bash
python scripts/mme_calculation --results_dir [PATH_TO_RESPONSE_FILE]
```

#### LLaVA-Bench
Download [LLaVA-Bench (In-the-Wild)](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) and run the following command under the root directory:
```bash
python scripts/llavabench_vqa.py --model_name ['LLaVA-7B', 'MiniGPT4', 'mPLUG_Owl2'] --model_path  --image-folder liuhaotian/llava-bench-in-the-wild/images --question_file liuhaotian/llava-bench-in-the-wild/questions.jsonl
```
The response file will be stored in `eval/llavabench_answer/{os.path.basename(args.model_path)}`. To calculate the score, run:
```bash
python scripts/llavabench_gpt_eval.py --raw_answer_path [PATH_TO_RAW_MODEL_RESPONSE] --edit_answer_path [PATH_TO_EDIT_MODEL_RESPONSE] --images_dir [PATH_TO_IMAGES] --api_key [API_KEY] --base_url [URL]
```
GPT-4V evaluated responses will be saved under `eval/llavabench/results/` as `.log` file.

## Experiments
- **Nullu mitigates the object hallucination issue across different LVLM families.**
<p align="center">
  <img src="figs/table12.png" alt="exp1" style="max-width: 60%; height: auto;">
</p>

*table 12. Results on POPE. Original denotes direct sampling for LVLMs, whereas Nullu refers to edit the model with the proposed method.*

- **Results in general LVLM benchmarks, highlighting its wide-ranging applicability.**
<p align="center">
  <img src="figs/figure5.png" alt="exp2" style="max-width: 60%; height: auto;">
</p>

*figure 5. MME full set results on LLaVA-1.5. From the results we see that Nullu leads to consistent improvements of LVLM in both perception tasks and recognition capacities.*
<p align="center" width="80%">
    <img src="figs/table4.png" alt="GPT4V aided evaluation" style="width: 50%; min-width: 200px; display: block; margin: auto;"></a>
</p>

*table 4. Results of GPT-4V-aided evaluation on LLaVA-Bench following the setting in [VCD](https://arxiv.org/abs/2311.16922). Both metrics are on a scale of 10.*

- **Please refer to [our paper](http://arxiv.org/abs/2412.13817) for detailed experimental results.**

### Citation
If you find this work useful or use our codes in your own research, please use the following bibtex:
```
@inproceedings{yang2025nullu,
  title={Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection},
  author={Yang, Le and Zheng, Ziwei and Chen, Boxu and Zhao, Zhengyu and Lin, Chenhao and Shen, Chao},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2025}
}
```

## Acknowledgement
This repository builds upon the contributions of [ProFS](https://arxiv.org/abs/2405.13967), [LLaVA 1.5](https://github.com/haotian-liu/LLaVA), [mPLUG_Owl2](https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2), [MiniGPT-4](https://minigpt-4.github.io/.
). Thanks for their awesome works.

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