MarineEval: Assessing the Marine Intelligence of Vision-Language Models
Paper
•
2512.21126
•
Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/src/services/worker/DocumentInterpretation/BookUnderstanding/images/reef_fish_identification_page_239.png'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1953, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2147, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1409, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 175, in decode_example
image = PIL.Image.open(path)
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3431, in open
fp = builtins.open(filename, "rb")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/src/services/worker/DocumentInterpretation/BookUnderstanding/images/reef_fish_identification_page_239.png'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for MarineEval
The work introduces MarineEval, the first large-scale benchmark specifically designed to evaluate the marine understanding capabilities of Vision-Language Models (VLMs). MarineEval contains 2,000 expert-verified image-based question–answer pairs across 7 task dimensions and 20 domain-specific capacity dimensions, emphasizing specialized marine knowledge, visual reasoning, and real-world complexity. Through comprehensive benchmarking of 17 state-of-the-art VLMs, the study reveals that existing general-purpose models perform poorly on marine tasks, including particularly in spatial reasoning, species identification, and ecological understanding, highlighting the need for domain-aware training and evaluation. This resource aims to foster progress toward domain-expert VLMs capable of advancing research and conservation in marine science.
Dataset
Dataset Structure
The dataset structure is as follows:
dataset/
├── dimension 1
│ ├── sub dimension 1
│ │ ├── images/
│ │ ├── data.json
│ ├── sub dimension 1
│ │ ├── images/
│ │ ├── data.json
├── dimension 2
│ ├── sub dimension 1
│ │ ├── images/
│ │ ├── data.json
...
JSON File Structure
Each data.json file follows this structure:
"data": [
{
"id": 0,
"question": "string",
"answers": [
{
"answer": "string",
}
],
"qusetion_format": 0
}
]
Question Formats
The MarineEval dataset includes five types of question formats:
| Code | Question Format | Description |
|---|---|---|
| 0 | Yes-No Question | Models make binary classification to determine whether a statement is true or false. |
| 1 | Multiple Choice Question | Models select one or more than one correct option from at least four choices |
| 2 | Summarization Question | Models are asked to summarize the insight of the given image in free format |
| 3 | Localization Question | Models are asked to provide bounding box of target objects in COCO format. |
| 4 | Closed-Form (Loose) | Models response in a restricted format, which is evaluated with flexible semantic matching by LLM. |
| 5 | Closed-Form (Strict) | Models respond in a restricted format, which requires an exact match with the ground truth. |
Citation
@misc{wong2025marineevalassessingmarineintelligence,
title={MarineEval: Assessing the Marine Intelligence of Vision-Language Models},
author={YuK-Kwan Wong and Tuan-An To and Jipeng Zhang and Ziqiang Zheng and Sai-Kit Yeung},
year={2025},
eprint={2512.21126},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.21126},
}
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