README.md

metadata

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

Project Page | Paper | Model

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 training/benchmark scripts
Parquet	parquet/*.parquet	embedded (raw bytes)	🤗 datasets (load_dataset) and lmms-eval
TSV	multihopspatial.tsv	embedded (base64)	VLMEvalKit

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

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:

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

Usage

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	MultihopSpatial (this work)
COCO Images	COCO Terms of Use	MS-COCO
PACO-Ego4D Images	Ego4D License	PACO / Ego4D

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

Citation

@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 or open an issue in this repository.