| --- |
| license: cc-by-4.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - multi-image |
| - hallucination |
| - benchmark |
| - vision-language-model |
| - multimodal |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # MIHBench: Multi-Image Hallucination Benchmark |
|
|
| **Paper**: [MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?](https://arxiv.org/abs/2505.xxxxx) | ACM Multimedia 2025 |
|
|
| ## Overview |
|
|
| MIHBench is a comprehensive benchmark for evaluating multi-image understanding and hallucination in Multi-modal Large Language Models (MLLMs). It contains **3,200 samples** across **4 tasks** (800 samples each), with each sample containing 2-4 images. |
|
|
| ## Tasks |
|
|
| | Task | # Images | Description | |
| |------|----------|-------------| |
| | **Count** | 2 | Determine whether the same number of a target object appears in both images. 400 samples include injected distracting objects. | |
| | **Existence (Adversarial)** | 3 | Determine whether a target object exists in all images, with adversarially selected objects (rare, confusing). | |
| | **Existence (Popular)** | 3 | Determine whether a target object exists in all images, using commonly known objects. | |
| | **Existence (Random)** | 3 | Determine whether a target object exists in all images, using randomly selected objects. | |
|
|
| > **Note**: A 5th task (ID Consistency) will be added in a future update. |
|
|
| ## Dataset Schema |
|
|
| ### Common columns (all tasks) |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `images` | `list[image]` | 2-4 images (PIL Image objects) | |
| | `question` | `str` | Natural language question about the images | |
| | `label` | `str` | Ground truth answer (`"yes"` or `"no"`) | |
| | `task` | `str` | Task identifier | |
| | `num_images` | `int` | Number of images in the sample | |
| | `image_names` | `list[str]` | Source image filenames | |
|
|
| ### Additional columns (Count task only) |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `injected` | `bool` | Whether distracting objects were injected into the question | |
| | `object_counts` | `str` | JSON string mapping image identifiers to object counts (e.g., `'{"A": 1, "B": 1}'`) | |
|
|
| ## Data Splits |
|
|
| Each task is a separate configuration/split with 800 samples (400 `"yes"`, 400 `"no"`). |
|
|
| ## Image Sources |
|
|
| - **Tasks 1-4**: COCO (Common Objects in Context) dataset |
| - **Task 5** (ID Consistency, coming soon): CO3D dataset |
|
|
| ## Citation |
|
|
| If you use MIHBench in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{mihbench2025, |
| title={MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?}, |
| author={}, |
| booktitle={Proceedings of the ACM Multimedia 2025}, |
| year={2025} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. The underlying images are from COCO and CO3D, which have their own licenses. |
|
|
