Datasets:

byougert
/

MInterPEDES

---
language:
- en
license: cc-by-nc-4.0
pretty_name: MInterPEDES
task_categories:
- text-retrieval
tags:
- person-retrieval
- person-re-identification
- interactive-retrieval
size_categories:
- 10K<n<100K
---
# MInterPEDES

MInterPEDES is a multi-turn multimodal conversational dataset for **Multimodal Interactive Person Retrieval (MInterPR)**. It was introduced in the ICML 2026 paper **“Interactive Person Retrieval via Multi-Turn Multimodal Conversation.”**

MInterPR extends text-based and chat-based person retrieval by allowing a user to inspect retrieved reference images and provide corrective natural-language feedback about how each reference differs from the target person. MInterPEDES augments the question-answering dialogues of ChatPedes with reference-image paths and corrective feedback for studying this setting.

- **Dataset repository:** [byougert/MInterPEDES](https://huggingface.co/datasets/byougert/MInterPEDES)
- **Official code:** [Flame-Chasers/MNEMO](https://github.com/Flame-Chasers/MNEMO)
- **Original image source:** [CUHK-PEDES](https://github.com/ShuangLI59/Person-Search-with-Natural-Language-Description)

## Repository Contents

This repository releases **annotations only**:

```text
MInterPEDES/
├── README.md
├── train_reid.jsonl
└── test_reid.jsonl
```

**The original CUHK-PEDES images are not included or redistributed.** The paths stored in the annotation files are relative paths into a separately obtained copy of CUHK-PEDES.

## Dataset Statistics

Each target image is associated with two multimodal dialogue records constructed using two different reference-image sampling strategies. Consequently, the number of annotation records is twice the number of target images.

| Split | Identities | Images | Dialogues |
| --- | ---: | ---: | ---: |
| Train | 11,003 | 34,054 | 68,108 |
| Test | 1,000 | 3,074 | 6,148 |
| **Total** | **12,003** | **37,128** | **74,256** |

The training and test splits are identity-disjoint and follow the CUHK-PEDES split protocol.

## Data Format

Both files use JSON Lines format: each line is one JSON object representing one multimodal dialogue associated with a target image.

| Field | Type | Description |
| --- | --- | --- |
| `file_path` | string | The target image path. |
| `id` | integer | Person identity label. |
| `t_conversation_length` | integer | Number of text question-answer turns after the initial description. |
| `i_conversation_length` | integer | Number of visual feedback turns. |
| `conversations` | list of objects | Ordered dialogue messages. Every object contains `from` and `value`. |
| `image` | list of strings | Relative paths of the reference images, in the same order as the image turns in `conversations`. |

The ordered `conversations` sequence has the following structure:

1. The first `human` message is the initial description of the target person.
2. The next `2 × t_conversation_length` messages alternate between a `gpt` question and a `human` answer.
3. The final `2 × i_conversation_length` messages alternate between a `gpt` image marker (`<image>`) and the corresponding `human` corrective feedback.

Thus, for each record:

```text
len(conversations) = 1 + 2 * t_conversation_length + 2 * i_conversation_length
len(image) = i_conversation_length
```

The `from` labels follow the interaction convention used by the MNEMO data loader: the model asks a question or presents a retrieved image, and the user supplies an answer or corrective feedback.

Each `file_path` occurs exactly twice within its split. The released JSONL schema does not contain a separate field naming the sampling strategy; use the official MNEMO data loader when strategy-specific ordering is required.

## Obtaining the Images

This repository does **not** grant access to, license, or redistribute the CUHK-PEDES images. To reconstruct the complete dataset:

1. Visit the [official CUHK-PEDES repository](https://github.com/ShuangLI59/Person-Search-with-Natural-Language-Description).
2. Follow its current access procedure and terms of use to obtain the images. The upstream page states that access is for academic use and that requests should be made with a university email because of licensing issues.
3. Keep the directory names referenced by `file_path` and `image`, or adapt them through the data-preparation instructions in the [MNEMO repository](https://github.com/Flame-Chasers/MNEMO).

Users are solely responsible for complying with the CUHK-PEDES access conditions and any other applicable upstream terms.

## Usage

### Load from the Hugging Face Hub

```bash
pip install -U datasets
```

```python
from datasets import load_dataset
dataset = load_dataset("byougert/MInterPEDES")
train_set = dataset["train"]
test_set = dataset["test"]
print(train_set[0])
```

The image-related fields remain relative path strings because the image files are not included.

### Load JSONL directly

```python
import json
with open("train_reid.jsonl", "r", encoding="utf-8") as f:
    sample = json.loads(next(f))
print(sample["file_path"])
print(sample["conversations"])
print(sample["image"])
```

### Resolve image paths

After obtaining CUHK-PEDES, resolve target and reference images relative to the local image root:

```python
from pathlib import Path
from datasets import load_dataset
sample = load_dataset("byougert/MInterPEDES", split="train")[0]
# This directory must directly contain the subdirectories referenced by
# file_path and image, such as CUHK01 or train_query.
image_root = Path("/path/to/CUHK-PEDES-image-root")
target_image = image_root / sample["file_path"]
reference_images = [image_root / path for path in sample["image"]]
```

For the authoritative training, evaluation, and directory-preparation workflow, use the [official MNEMO code](https://github.com/Flame-Chasers/MNEMO).

## License

The original annotation content released in `train_reid.jsonl` and `test_reid.jsonl` is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/) (**CC BY-NC 4.0**).

This license applies to the **MInterPEDES annotations only**. It does not apply to CUHK-PEDES images or other upstream materials. The underlying images remain governed by the original CUHK-PEDES access conditions and terms of use. Users must satisfy both the annotation license and the applicable upstream terms.

## Citation

If you use MInterPEDES, please cite the ICML 2026 paper:

```bibtex
@inproceedings{bai2026interactive,
  title     = {Interactive Person Retrieval via Multi-Turn Multimodal Conversation},
  author    = {Bai, Yang and Wang, Tingfeng and Yang, Bin and Cao, Min and Wang, Jinqiao and Ye, Mang},
  booktitle = {Forty-third International Conference on Machine Learning},
  year      = {2026}
}
```

Because MInterPEDES builds on ChatPedes and CUHK-PEDES, please also cite the relevant upstream works:

```bibtex
@inproceedings{bai2025chat,
  author    = {Bai, Yang and Ji, Yucheng and Cao, Min and Wang, Jinqiao and Ye, Mang},
  title     = {Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages     = {3952--3962},
  year      = {2025}
}
@inproceedings{li2017person,
  author    = {Li, Shuang and Xiao, Tong and Li, Hongsheng and Zhou, Bolei and Yue, Dayu and Wang, Xiaogang},
  title     = {Person Search With Natural Language Description},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages     = {1970--1979},
  year      = {2017}
}
```

## Acknowledgements

MInterPEDES is built upon CUHK-PEDES. We thank the authors and maintainers of those resources and the related open-source projects.

	---
	language:
	- en
	license: cc-by-nc-4.0
	pretty_name: MInterPEDES
	task_categories:
	- text-retrieval
	tags:
	- person-retrieval
	- person-re-identification
	- interactive-retrieval
	size_categories:
	- 10K<n<100K
	---

	# MInterPEDES

	MInterPEDES is a multi-turn multimodal conversational dataset for Multimodal Interactive Person Retrieval (MInterPR). It was introduced in the ICML 2026 paper “Interactive Person Retrieval via Multi-Turn Multimodal Conversation.”

	MInterPR extends text-based and chat-based person retrieval by allowing a user to inspect retrieved reference images and provide corrective natural-language feedback about how each reference differs from the target person. MInterPEDES augments the question-answering dialogues of ChatPedes with reference-image paths and corrective feedback for studying this setting.

	- Dataset repository: [byougert/MInterPEDES](https://huggingface.co/datasets/byougert/MInterPEDES)
	- Official code: [Flame-Chasers/MNEMO](https://github.com/Flame-Chasers/MNEMO)
	- Original image source: [CUHK-PEDES](https://github.com/ShuangLI59/Person-Search-with-Natural-Language-Description)

	## Repository Contents

	This repository releases annotations only:

	```text
	MInterPEDES/
	├── README.md
	├── train_reid.jsonl
	└── test_reid.jsonl
	```

	The original CUHK-PEDES images are not included or redistributed. The paths stored in the annotation files are relative paths into a separately obtained copy of CUHK-PEDES.

	## Dataset Statistics

	Each target image is associated with two multimodal dialogue records constructed using two different reference-image sampling strategies. Consequently, the number of annotation records is twice the number of target images.

	\| Split \| Identities \| Images \| Dialogues \|
	\| --- \| ---: \| ---: \| ---: \|
	\| Train \| 11,003 \| 34,054 \| 68,108 \|
	\| Test \| 1,000 \| 3,074 \| 6,148 \|
	\| Total \| 12,003 \| 37,128 \| 74,256 \|

	The training and test splits are identity-disjoint and follow the CUHK-PEDES split protocol.

	## Data Format

	Both files use JSON Lines format: each line is one JSON object representing one multimodal dialogue associated with a target image.

	\| Field \| Type \| Description \|
	\| --- \| --- \| --- \|
	\| `file_path` \| string \| The target image path. \|
	\| `id` \| integer \| Person identity label. \|
	\| `t_conversation_length` \| integer \| Number of text question-answer turns after the initial description. \|
	\| `i_conversation_length` \| integer \| Number of visual feedback turns. \|
	\| `conversations` \| list of objects \| Ordered dialogue messages. Every object contains `from` and `value`. \|
	\| `image` \| list of strings \| Relative paths of the reference images, in the same order as the image turns in `conversations`. \|

	The ordered `conversations` sequence has the following structure:

	1. The first `human` message is the initial description of the target person.
	2. The next `2 × t_conversation_length` messages alternate between a `gpt` question and a `human` answer.
	3. The final `2 × i_conversation_length` messages alternate between a `gpt` image marker (`<image>`) and the corresponding `human` corrective feedback.

	Thus, for each record:

	```text
	len(conversations) = 1 + 2 * t_conversation_length + 2 * i_conversation_length
	len(image) = i_conversation_length
	```

	The `from` labels follow the interaction convention used by the MNEMO data loader: the model asks a question or presents a retrieved image, and the user supplies an answer or corrective feedback.

	Each `file_path` occurs exactly twice within its split. The released JSONL schema does not contain a separate field naming the sampling strategy; use the official MNEMO data loader when strategy-specific ordering is required.

	## Obtaining the Images

	This repository does not grant access to, license, or redistribute the CUHK-PEDES images. To reconstruct the complete dataset:

	1. Visit the [official CUHK-PEDES repository](https://github.com/ShuangLI59/Person-Search-with-Natural-Language-Description).
	2. Follow its current access procedure and terms of use to obtain the images. The upstream page states that access is for academic use and that requests should be made with a university email because of licensing issues.
	3. Keep the directory names referenced by `file_path` and `image`, or adapt them through the data-preparation instructions in the [MNEMO repository](https://github.com/Flame-Chasers/MNEMO).

	Users are solely responsible for complying with the CUHK-PEDES access conditions and any other applicable upstream terms.

	## Usage

	### Load from the Hugging Face Hub

	```bash
	pip install -U datasets
	```

	```python
	from datasets import load_dataset

	dataset = load_dataset("byougert/MInterPEDES")
	train_set = dataset["train"]
	test_set = dataset["test"]

	print(train_set[0])
	```

	The image-related fields remain relative path strings because the image files are not included.

	### Load JSONL directly

	```python
	import json

	with open("train_reid.jsonl", "r", encoding="utf-8") as f:
	sample = json.loads(next(f))

	print(sample["file_path"])
	print(sample["conversations"])
	print(sample["image"])
	```

	### Resolve image paths

	After obtaining CUHK-PEDES, resolve target and reference images relative to the local image root:

	```python
	from pathlib import Path
	from datasets import load_dataset

	sample = load_dataset("byougert/MInterPEDES", split="train")[0]

	# This directory must directly contain the subdirectories referenced by
	# file_path and image, such as CUHK01 or train_query.
	image_root = Path("/path/to/CUHK-PEDES-image-root")

	target_image = image_root / sample["file_path"]
	reference_images = [image_root / path for path in sample["image"]]
	```

	For the authoritative training, evaluation, and directory-preparation workflow, use the [official MNEMO code](https://github.com/Flame-Chasers/MNEMO).

	## License

	The original annotation content released in `train_reid.jsonl` and `test_reid.jsonl` is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/) (CC BY-NC 4.0).

	This license applies to the MInterPEDES annotations only. It does not apply to CUHK-PEDES images or other upstream materials. The underlying images remain governed by the original CUHK-PEDES access conditions and terms of use. Users must satisfy both the annotation license and the applicable upstream terms.

	## Citation

	If you use MInterPEDES, please cite the ICML 2026 paper:

	```bibtex
	@inproceedings{bai2026interactive,
	title = {Interactive Person Retrieval via Multi-Turn Multimodal Conversation},
	author = {Bai, Yang and Wang, Tingfeng and Yang, Bin and Cao, Min and Wang, Jinqiao and Ye, Mang},
	booktitle = {Forty-third International Conference on Machine Learning},
	year = {2026}
	}
	```

	Because MInterPEDES builds on ChatPedes and CUHK-PEDES, please also cite the relevant upstream works:

	```bibtex
	@inproceedings{bai2025chat,
	author = {Bai, Yang and Ji, Yucheng and Cao, Min and Wang, Jinqiao and Ye, Mang},
	title = {Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment},
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages = {3952--3962},
	year = {2025}
	}

	@inproceedings{li2017person,
	author = {Li, Shuang and Xiao, Tong and Li, Hongsheng and Zhou, Bolei and Yue, Dayu and Wang, Xiaogang},
	title = {Person Search With Natural Language Description},
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages = {1970--1979},
	year = {2017}
	}
	```

	## Acknowledgements

	MInterPEDES is built upon CUHK-PEDES. We thank the authors and maintainers of those resources and the related open-source projects.