README.md

---
pretty_name: IBCBench
language:
- en
license: other
task_categories:
- other
tags:
- image-retrieval
- multi-image-retrieval
- multimodal-agents
- image-bundle-composition
- visual-history
- yfcc100m
configs:
- config_name: default
  data_files:
  - split: test
    path: queries.jsonl
---

# IBCBench: Image Bundle Composition Benchmark

IBCBench is the benchmark introduced in **Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching**, accepted to the **EMNLP 2026 Main Conference**.

[**GitHub**](https://github.com/LaVieEnRose365/Image-Bundle-Composition)

## Overview

Image Bundle Composition (IBC) shifts image retrieval from independently ranking images to dynamically composing a compact, cohesive bundle whose images jointly satisfy relational, temporal, spatial, or narrative constraints. The target bundles are not predefined in the image pool, so systems must reason over non-decomposable bundle-level relevance in a combinatorial search space.

IBCBench contains **667 human-verified queries** evaluated against a pool of **109,467 images from 57 users**. Each ground-truth bundle contains 3--5 images. The benchmark was built using a semi-automated candidate mining and verification pipeline followed by expert human review.

## Dataset Statistics

| Statistic | Value |
|:--|--:|
| Queries | 667 |
| Images | 109,467 |
| Users | 57 |
| Bundle size: 3 images | 162 (24.3%) |
| Bundle size: 4 images | 215 (32.2%) |
| Bundle size: 5 images | 290 (43.5%) |
| Same-location dynamics | 52.5% |
| Cross-location structures | 47.5% |

## Download

Download the complete benchmark from the Hugging Face Hub:

```bash
hf download CyberDancer/IBCBench --repo-type dataset --local-dir IBCBench
```

Then extract the image pool:

```bash
unzip IBCBench/images.zip -d IBCBench
```

Alternatively, the included `download_images.py` script can reconstruct the image pool from `photo_ids/`:

```bash
python IBCBench/download_images.py \
  --photo-ids-path IBCBench/photo_ids \
  --images-path IBCBench/images
```

## File Structure

```text
IBCBench/
├── queries.jsonl              # 667 IBC queries and ground-truth bundles
├── metadata/
│   └── {user_id}.jsonl        # Photo metadata for each user
├── photo_ids/
│   └── {user_id}.txt          # Photo IDs and storage hashes for each user
├── images.zip                # images/{user_id}/{photo_id}.jpg
└── download_images.py        # Alternative image downloader
```

## Query Format

Each line in `queries.jsonl` is one query and its ground-truth image bundle:

```json
{
  "query_id": "10287726@N02_s18_w0_n3",
  "query": "Find a bundle documenting the same spectator's Olympic viewing experience: ...",
  "image_ids": ["7797992908", "7797994344", "7797998284"]
}
```

| Field | Type | Description |
|:--|:--|:--|
| `query_id` | string | Unique query identifier. Its prefix is the YFCC user ID. |
| `query` | string | Natural-language bundle query in English. |
| `image_ids` | list[string] | Ground-truth set of 3--5 YFCC photo IDs. |

## Photo Metadata Format

Each line in `metadata/{user_id}.jsonl` describes one image:

```json
{
  "photo_id": "4517621778",
  "metadata": {
    "taken_time": "2010-04-10 13:52:57",
    "longitude": -1.239802,
    "latitude": 51.754123,
    "accuracy": 16.0,
    "address": "...",
    "capturedevice": "Panasonic DMC-TZ5"
  }
}
```

The location, address, accuracy, and capture-device fields are optional and may be absent. Album identifiers are intentionally excluded from the benchmark.

Each non-empty line in `photo_ids/{user_id}.txt` has the following tab-separated form:

```text
{photo_id}\t{storage_hash}
```

## Shared Image Pool and Attribution

IBCBench uses the same image pool, metadata, and photo-ID mapping as [DISBench](https://huggingface.co/datasets/RUC-NLPIR/DISBench), which is derived from the public [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/) collection. IBCBench contributes a separate set of 667 queries and ground-truth bundles for the Image Bundle Composition task.

Please also cite DISBench and YFCC100M when using the shared image pool.

## Ethical Considerations

The source images were publicly distributed under Creative Commons licenses. IBCBench is intended for research on multimodal retrieval and relational reasoning, particularly user-centric organization of personal photo collections. It is not intended for unauthorized surveillance, identity profiling, or analysis of private third-party collections.

Although the source media is public, the benchmark includes spatiotemporal metadata. Users should handle this information responsibly and follow the terms attached to each original image.

## License

The IBCBench query annotations and BundleWeaver project are released under the [Apache License 2.0](https://github.com/LaVieEnRose365/Image-Bundle-Composition/blob/main/LICENSE). The images retain the individual Creative Commons licenses attached to the corresponding YFCC100M records; downstream users are responsible for checking and following those terms. The included DISBench-derived helper and shared files retain their upstream terms.

## Citation

```bibtex
@inproceedings{shan2026weaving,
  title     = {Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching},
  author    = {Shan, Rong and Xu, Tianyi and Zheng, Congmin and Chen, Wenteng and Zhu, Jiachen and Wu, Junjie and Zeng, Dun and Wang, Teng and Liu, Weiwen and Zhang, Changwang and Zhang, Weinan and Wang, Jun and Lin, Jianghao},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}
```

DISBench:

```bibtex
@misc{deng2026deepimagesearch,
  title         = {DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories},
  author        = {Deng, Chenlong and Deng, Mengjie and Wu, Junjie and Zeng, Dun and Wang, Teng and Xie, Qingsong and Huang, Jiadeng and Ma, Shengjie and Zhang, Changwang and Wang, Zhaoxiang and Wang, Jun and Zhu, Yutao and Dou, Zhicheng},
  year          = {2026},
  eprint        = {2602.10809},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}
```

YFCC100M:

```bibtex
@article{thomee2016yfcc100m,
  title   = {YFCC100M: The New Data in Multimedia Research},
  author  = {Thomee, Bart and Shamma, David A. and Friedland, Gerald and Elizalde, Benjamin and Ni, Karl and Poland, Douglas and Borth, Damian and Li, Li-Jia},
  journal = {Communications of the ACM},
  volume  = {59},
  number  = {2},
  pages   = {64--73},
  year    = {2016}
}
```