---
license: apache-2.0
task_categories:
  - visual-question-answering
  - image-text-to-text
language:
  - en
tags:
  - spatial-reasoning
  - multi-hop
  - grounding
  - vision-language
  - benchmark
  - VQA
  - bounding-box
pretty_name: MultihopSpatial
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: parquet/train-*.parquet
      - split: test
        path: parquet/test-*.parquet
  - config_name: benchmark
    data_files:
      - split: test
        path: parquet/test-*.parquet
---

# [ECCV 2026] MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Models

<p align="center">
  <img src="teaser_2.png" width="100%" alt="MultihopSpatial Benchmark Overview">
</p>

<p align="center">
  <a href="https://youngwanlee.github.io/multihopspatial"><b>Project Page</b></a> |
  <a href="https://arxiv.org/abs/2603.18892"><b>Paper</b></a> |
  <a href="https://huggingface.co/etri-vilab/MultiHopSpatial-Qwen3-VL-4B-Instruct"><b>Model</b></a>
</p>

## Overview

**MultihopSpatial** is a benchmark designed to evaluate whether vision-language models (VLMs) demonstrate robustness in **multi-hop compositional spatial reasoning**. Unlike existing benchmarks that only assess single-step spatial relations, MultihopSpatial features queries with **1 to 3 reasoning hops** paired with **visual grounding evaluation**, exposing a critical blind spot: models achieving high multiple-choice accuracy often lack proper spatial localization.

All 4,500 benchmark QA pairs and bounding boxes are **strictly annotated by ten trained human experts** with an inter-rater agreement of 90% (Krippendorff's α = 0.90).

## Key Features

- **Multi-hop Composition**: Tests 1-hop, 2-hop, and 3-hop sequential spatial reasoning, mirroring real-world embodied AI needs.
- **Grounded Evaluation**: Addresses the "lucky guess" problem — models must both select the correct answer AND localize it via bounding box (Acc@50IoU).
- **Perspective-taking**: Includes both ego-centric and exo-centric viewpoints.
- **Three Spatial Categories**: Attribute (ATT), Position (POS), and Relation (REL), composable into multi-hop questions.
- **Training Data**: MultihopSpatial-Train (6,791 samples) supports post-training via reinforcement learning (e.g., GRPO).

## Dataset Statistics

### MultihopSpatial

| | **Ego-centric** | **Exo-centric** | **Total** |
|---|:---:|:---:|:---:|
| **1-hop** | 750 | 750 | 1,500 |
| **2-hop** | 750 | 750 | 1,500 |
| **3-hop** | 750 | 750 | 1,500 |
| **Total** | 2,250 | 2,250 | **4,500** |


### Spatial Reasoning Compositions

| **Hop** | **Categories** |
|---|---|
| 1-hop | ATT, POS, REL |
| 2-hop | ATT+POS, ATT+REL, POS+REL |
| 3-hop | ATT+POS+REL |

## Data Fields

| Field | Type | Description |
|---|---|---|
| `id` | `int` | Unique sample identifier |
| `image_path` | `string` | Image filename (e.g., `000000303219.jpg` or `01ce4fd6-..._002114.jpeg`) |
| `image_resolution` | `string` | Image resolution in `WxH` format |
| `view` | `string` | Viewpoint type: `"ego"` (ego-centric) or `"exo"` (exo-centric) |
| `hop` | `string` | Reasoning complexity: `"1hop"`, `"2hop"`, or `"3hop"` |
| `question` | `string` | The spatial reasoning question in plain text with multiple-choice options |
| `question_tag` | `string` | Same question with spatial reasoning type tags (`<ATT>`, `<POS>`, `<REL>`) annotated inline |
| `answer` | `string` | The correct answer choice (e.g., `"(c) frame of the reed picture"`) |
| `bbox` | `list[float]` | Bounding box `[x, y, width, height]` of the answer object in pixel coordinates |

### `question` vs `question_tag`

- **`question`**: Clean natural language question, e.g.,
  > *"From the perspective of the woman holding the remote control, which object is on her right?"*

- **`question_tag`**: Same question with spatial reasoning tags marking which type of reasoning each part requires, e.g.,
  > *"From the perspective of the woman holding the remote control, which object is **\<POS\>on her right\</POS\>**?"*

  Tags: `<ATT>...</ATT>` (Attribute), `<POS>...</POS>` (Position), `<REL>...</REL>` (Relation)

## Data Structure

```
MultihopSpatial/
├── README.md
├── teaser_2.png
├── data/                              # Raw annotations + images
│   ├── multihop_test_4500.json
│   ├── multihop_train_6791.json
│   └── images/
│       ├── 000000303219.jpg
│       ├── 000000022612.jpg
│       ├── 01ce4fd6-197a-4792-8778-775b03780369_002114.jpeg
│       └── ...
├── parquet/                           # datasets-native (images embedded)
│   ├── train-00000-of-00001.parquet
│   └── test-00000-of-00001.parquet
└── multihopspatial.tsv               # VLMEvalKit format (base64 images)
```

## Data Formats

The same benchmark is shipped in three formats so it drops into different evaluation stacks without any conversion. **All three describe the identical 4,500 test samples** — pick whichever your tool expects.

| Format | Files | Images | Primarily used by |
|---|---|---|---|
| **Raw JSON + images** | `data/*.json` + `data/images/` | separate `.jpg`/`.jpeg` files | Custom pipelines; the [`msrbench`](https://github.com/youngwanLEE/msrbench) training/benchmark scripts |
| **Parquet** | `parquet/*.parquet` | embedded (raw bytes) | 🤗 `datasets` (`load_dataset`) and **[lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)** |
| **TSV** | `multihopspatial.tsv` | embedded (base64) | **[VLMEvalKit](https://github.com/open-compass/VLMEvalKit)** |

> [!NOTE]
> The Parquet and TSV files embed the **original image bytes verbatim** (no re-encoding), so grounding IoU is identical across harnesses. The `test` split is shared by the `default` and `benchmark` configs.

### Parquet — 🤗 `datasets` / lmms-eval

```python
from datasets import load_dataset

# Full dataset (train + test)
ds = load_dataset("etri-vilab/MultihopSpatial")

# Benchmark config (test split only) — used by the lmms-eval task
bench = load_dataset("etri-vilab/MultihopSpatial", "benchmark", split="test")
```

### TSV — VLMEvalKit

VLMEvalKit auto-downloads the TSV to `~/LMUData/` on first run:

```bash
python run.py --data MultihopSpatial --model <your_model>
```

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("etri-vilab/MultihopSpatial")

# Access splits
test_data = dataset["test"]
train_data = dataset["train"]

# Example
sample = test_data[0]
print(sample["question"])
# "From the perspective of the woman holding the remote control, which object is on her right? ..."
print(sample["answer"])
# "(c) frame of the reed picture"
print(sample["bbox"])
# [52.86, 38.7, 70.95, 97.83]
print(sample["hop"])
# "1hop"
```


## Image Sources & License

| Component | License | Source |
|---|---|---|
| **VQA Annotations** (questions, answers, bounding boxes) | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | MultihopSpatial (this work) |
| **COCO Images** | [COCO Terms of Use](https://cocodataset.org/#termsofuse) | [MS-COCO](https://cocodataset.org/) |
| **PACO-Ego4D Images** | [Ego4D License](https://ego4ddataset.com/ego4d-data/license/) | [PACO](https://github.com/facebookresearch/paco) / [Ego4D](https://ego4ddataset.com/) |

> The images retain their original licenses. Our VQA annotations (questions, answers, bounding boxes, and metadata) are released under the Apache 2.0 License.

## Citation

```bibtex
@inproceedings{lee2026multihopspatial,
  title={MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Models},
  author={Lee, Youngwan and Jang, Soojin and Cho, Yoorhim and Lee, Seunghwan and Lee, Yong-Ju and Hwang, Sung Ju},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}
```

## Contact

For questions or issues, please visit the [Project Page](https://youngwanlee.github.io/multihopspatial) or open an issue in this repository.