This repo is for the paper Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models (ICML2024).

@inproceedings{
wu2024evaluating,
title={Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models},
author={Mingrui Wu and Jiayi Ji and Oucheng Huang and Jiale Li and Yuhang Wu and Xiaoshuai Sun and Rongrong Ji},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=xpSlt67vxQ}
}

Download R-Bench. The main annotation files include:

- image-level_filterd.json
- instance-level_filterd.json
- nocaps_pope_obj_random_image.json
- nocaps_pope_obj_popular_image.json
- nocaps_pope_obj_adversarial_image.json
- web_data

These files contain annotations for image-level, instance-level, pope-object, and web-data questions. For image-level and instance-level questions, we randomly sampled five subsets into the [type]_ids_[subset].json files.

Download the images from Open Image validation set (v4).

To run LVLM on R-Bench using the official inference script of the LVLMs, and format the result file as follows:

{"question_id": 0, "text":[model output]}
{"question_id": 1, "text":[model output]}
...

Tips: We provide instance-level question tools in utils.py. Please use the draw_mask and draw_box functions to draw the mask and box on input images, respectively. Additionally, use the instance_qs_construct function to reformat the instance questions.

And eval with,

sh eval.sh

The evaluation code is based on POPE.