Datasets:

Tarun
/

AbsVis

---
pretty_name: AbsVis
license: other
license_name: absvis-mixed-cc
license_link: LICENSE.md
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - image-to-text
  - visual-question-answering
  - zero-shot-image-classification
tags:
  - abstract-concepts
  - concreteness
  - vision-language-models
  - human-annotations
  - explanations
  - multimodal
  - psycholinguistics
  - yfcc100m
  - emnlp2025
configs:
  - config_name: images
    default: true
    data_files:
      - split: train
        path: data/images.parquet
  - config_name: annotations
    data_files:
      - split: train
        path: data/annotations.parquet
  - config_name: nouns
    data_files:
      - split: train
        path: data/nouns.parquet
---
# AbsVis: Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images

- **Paper:** [AbsVis – Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images](https://aclanthology.org/2025.emnlp-main.417/) (EMNLP 2025, Main Conference)
- **Authors:** Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde
- **Code & raw CSVs:** [github.com/TarunTater/AbsVis_Emnlp2025](https://github.com/TarunTater/AbsVis_Emnlp2025)

Abstract concepts like *mercy* and *peace* often lack clear visual grounding. AbsVis is a dataset of
**675 images** annotated with **concept–explanation pairs** from humans and two vision-language models
(Qwen2-VL-7B-Instruct and LLaVA-NeXT `llava-v1.6-mistral-7b-hf`). Each attributed concept comes with a
free-text explanation of *why* the image evokes it.

The images depict **225 English nouns** spanning the concreteness scale (75 abstract, 75 mid-range,
75 concrete; three images per noun), sourced from [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/).

Key findings from the paper:
- Humans attribute more varied concepts than VLMs.
- Concepts attributed by **both** humans and VLMs are the most preferred.
- Explanations clarify and strengthen the perceived attributions, for both humans and VLMs.
- VLMs can approximate human preferences, and DPO fine-tuning on these preferences improves alignment with preferred concept–explanation pairs.

## Quick start

```python
from datasets import load_dataset
images = load_dataset("Tarun/AbsVis", "images", split="train")             # 675 images + noun + attribution
annotations = load_dataset("Tarun/AbsVis", "annotations", split="train")   # concept–explanation pairs
nouns = load_dataset("Tarun/AbsVis", "nouns", split="train")               # 225 nouns with concreteness ratings
ex = images[0]
ex["image"]          # PIL image
ex["noun"], ex["category"], ex["concreteness"]
# all human explanations for this image
human = annotations.filter(lambda r: r["image_id"] == ex["image_id"] and r["source"] == "human")
```

## Configs and fields

### `images` (default): one row per image
| field | description |
|---|---|
| `image` | Original YFCC100M image (unmodified file) |
| `image_id` | YFCC100M photo hash; joins with `annotations.image_id` |
| `noun` | Target noun the image was selected for |
| `concreteness` | Concreteness rating of the noun (1 = most abstract, 5 = most concrete; Brysbaert et al., 2014) |
| `category` | `abstract`, `mid-range`, or `concrete` |
| `yfcc_photo_id` | Flickr / YFCC100M photo ID |
| `flickr_owner_id`, `flickr_owner_name` | Flickr account of the photographer (for attribution) |
| `flickr_title` | Original Flickr title |
| `flickr_page_url` | Original Flickr page |
| `license_name`, `license_url` | The image's Creative Commons license |

### `annotations`: one row per concept–explanation pair
| field | description |
|---|---|
| `image_id` | Image the concept was attributed to |
| `noun` | Target noun of that image |
| `source` | `human`, `qwen` (Qwen2-VL-7B-Instruct), or `llava` (llava-v1.6-mistral-7b-hf) |
| `concept` | Attributed (abstract) concept |
| `explanation` | Free-text explanation of how the image conveys the concept |

Human annotations come from 220 native English speakers recruited via Prolific: five annotators per
image, three concept–explanation pairs each, giving up to 15 per image. One-word explanations were
removed during quality control, so a few images have fewer than 15 (10 for 1 image, 13 for 7, 14 for 28).

### `nouns`: the 225 target nouns
`noun`, `concreteness`, `category`.

## Licensing

AbsVis combines two kinds of content, each under its own license:

- **Annotations, nouns and metadata** (everything except the image files) are released under
  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
- **Images** are redistributed unmodified from YFCC100M and keep **their original Flickr Creative Commons
  license**, given per image in `license_name` / `license_url`. Only these four licenses occur:
  CC BY 2.0, CC BY-SA 2.0, CC BY-NC 2.0, CC BY-NC-SA 2.0.

Some images are **NonCommercial**, so commercial use of the full image set is not permitted. If you
reuse or display an image, credit its photographer (`flickr_owner_name`) and link `flickr_page_url`
and `license_url`. See [LICENSE.md](LICENSE.md).

## Citation

If you use AbsVis, please cite:

```bibtex
@inproceedings{tater-etal-2025-absvis,
    title = "{A}bs{V}is {--} Benchmarking How Humans and Vision-Language Models ``See'' Abstract Concepts in Images",
    author = "Tater, Tarun  and
      Frassinelli, Diego  and
      Schulte im Walde, Sabine",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.417/",
    doi = "10.18653/v1/2025.emnlp-main.417",
    pages = "8260--8281",
    ISBN = "979-8-89176-332-6"
}
```

The images come from YFCC100M; please also cite:

```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}
}
```

## Related work

- Tater et al. (2024). [Unveiling the Mystery of Visual Attributes of Concrete and Abstract Concepts: Variability, Nearest Neighbors, and Challenging Categories](https://aclanthology.org/2024.emnlp-main.1203/). EMNLP 2024.
- Tater et al. (2024). [Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts](https://aclanthology.org/2024.cmcl-1.18/). CMCL 2024.
- Tater et al. (2022). [Concreteness vs. Abstractness: A Selectional Preference Perspective](https://aclanthology.org/2022.aacl-srw.13/). AACL-IJCNLP 2022 SRW.

## Contact

Tarun Tater: tarun.tater@ims.uni-stuttgart.de

	---
	pretty_name: AbsVis
	license: other
	license_name: absvis-mixed-cc
	license_link: LICENSE.md
	language:
	- en
	size_categories:
	- 10K<n<100K
	task_categories:
	- image-to-text
	- visual-question-answering
	- zero-shot-image-classification
	tags:
	- abstract-concepts
	- concreteness
	- vision-language-models
	- human-annotations
	- explanations
	- multimodal
	- psycholinguistics
	- yfcc100m
	- emnlp2025
	configs:
	- config_name: images
	default: true
	data_files:
	- split: train
	path: data/images.parquet
	- config_name: annotations
	data_files:
	- split: train
	path: data/annotations.parquet
	- config_name: nouns
	data_files:
	- split: train
	path: data/nouns.parquet
	---

	# AbsVis: Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images

	- Paper: [AbsVis – Benchmarking How Humans and Vision-Language Models "See" Abstract Concepts in Images](https://aclanthology.org/2025.emnlp-main.417/) (EMNLP 2025, Main Conference)
	- Authors: Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde
	- Code & raw CSVs: [github.com/TarunTater/AbsVis_Emnlp2025](https://github.com/TarunTater/AbsVis_Emnlp2025)

	Abstract concepts like mercy and peace often lack clear visual grounding. AbsVis is a dataset of
	675 images annotated with concept–explanation pairs from humans and two vision-language models
	(Qwen2-VL-7B-Instruct and LLaVA-NeXT `llava-v1.6-mistral-7b-hf`). Each attributed concept comes with a
	free-text explanation of why the image evokes it.

	The images depict 225 English nouns spanning the concreteness scale (75 abstract, 75 mid-range,
	75 concrete; three images per noun), sourced from [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/).

	Key findings from the paper:
	- Humans attribute more varied concepts than VLMs.
	- Concepts attributed by both humans and VLMs are the most preferred.
	- Explanations clarify and strengthen the perceived attributions, for both humans and VLMs.
	- VLMs can approximate human preferences, and DPO fine-tuning on these preferences improves alignment with preferred concept–explanation pairs.

	## Quick start

	```python
	from datasets import load_dataset

	images = load_dataset("Tarun/AbsVis", "images", split="train") # 675 images + noun + attribution
	annotations = load_dataset("Tarun/AbsVis", "annotations", split="train") # concept–explanation pairs
	nouns = load_dataset("Tarun/AbsVis", "nouns", split="train") # 225 nouns with concreteness ratings

	ex = images[0]
	ex["image"] # PIL image
	ex["noun"], ex["category"], ex["concreteness"]

	# all human explanations for this image
	human = annotations.filter(lambda r: r["image_id"] == ex["image_id"] and r["source"] == "human")
	```

	## Configs and fields

	### `images` (default): one row per image
	\| field \| description \|
	\|---\|---\|
	\| `image` \| Original YFCC100M image (unmodified file) \|
	\| `image_id` \| YFCC100M photo hash; joins with `annotations.image_id` \|
	\| `noun` \| Target noun the image was selected for \|
	\| `concreteness` \| Concreteness rating of the noun (1 = most abstract, 5 = most concrete; Brysbaert et al., 2014) \|
	\| `category` \| `abstract`, `mid-range`, or `concrete` \|
	\| `yfcc_photo_id` \| Flickr / YFCC100M photo ID \|
	\| `flickr_owner_id`, `flickr_owner_name` \| Flickr account of the photographer (for attribution) \|
	\| `flickr_title` \| Original Flickr title \|
	\| `flickr_page_url` \| Original Flickr page \|
	\| `license_name`, `license_url` \| The image's Creative Commons license \|

	### `annotations`: one row per concept–explanation pair
	\| field \| description \|
	\|---\|---\|
	\| `image_id` \| Image the concept was attributed to \|
	\| `noun` \| Target noun of that image \|
	\| `source` \| `human`, `qwen` (Qwen2-VL-7B-Instruct), or `llava` (llava-v1.6-mistral-7b-hf) \|
	\| `concept` \| Attributed (abstract) concept \|
	\| `explanation` \| Free-text explanation of how the image conveys the concept \|

	Human annotations come from 220 native English speakers recruited via Prolific: five annotators per
	image, three concept–explanation pairs each, giving up to 15 per image. One-word explanations were
	removed during quality control, so a few images have fewer than 15 (10 for 1 image, 13 for 7, 14 for 28).

	### `nouns`: the 225 target nouns
	`noun`, `concreteness`, `category`.

	## Licensing

	AbsVis combines two kinds of content, each under its own license:

	- Annotations, nouns and metadata (everything except the image files) are released under
	[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
	- Images are redistributed unmodified from YFCC100M and keep **their original Flickr Creative Commons
	license**, given per image in `license_name` / `license_url`. Only these four licenses occur:
	CC BY 2.0, CC BY-SA 2.0, CC BY-NC 2.0, CC BY-NC-SA 2.0.

	Some images are NonCommercial, so commercial use of the full image set is not permitted. If you
	reuse or display an image, credit its photographer (`flickr_owner_name`) and link `flickr_page_url`
	and `license_url`. See [LICENSE.md](LICENSE.md).

	## Citation

	If you use AbsVis, please cite:

	```bibtex
	@inproceedings{tater-etal-2025-absvis,
	title = "{A}bs{V}is {--} Benchmarking How Humans and Vision-Language Models ``See'' Abstract Concepts in Images",
	author = "Tater, Tarun and
	Frassinelli, Diego and
	Schulte im Walde, Sabine",
	editor = "Christodoulopoulos, Christos and
	Chakraborty, Tanmoy and
	Rose, Carolyn and
	Peng, Violet",
	booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
	month = nov,
	year = "2025",
	address = "Suzhou, China",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2025.emnlp-main.417/",
	doi = "10.18653/v1/2025.emnlp-main.417",
	pages = "8260--8281",
	ISBN = "979-8-89176-332-6"
	}
	```

	The images come from YFCC100M; please also cite:

	```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}
	}
	```

	## Related work

	- Tater et al. (2024). [Unveiling the Mystery of Visual Attributes of Concrete and Abstract Concepts: Variability, Nearest Neighbors, and Challenging Categories](https://aclanthology.org/2024.emnlp-main.1203/). EMNLP 2024.
	- Tater et al. (2024). [Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts](https://aclanthology.org/2024.cmcl-1.18/). CMCL 2024.
	- Tater et al. (2022). [Concreteness vs. Abstractness: A Selectional Preference Perspective](https://aclanthology.org/2022.aacl-srw.13/). AACL-IJCNLP 2022 SRW.

	## Contact

	Tarun Tater: tarun.tater@ims.uni-stuttgart.de