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 (EMNLP 2025, Main Conference)
Authors: Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde
Code & raw CSVs: 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.

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

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.
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.

Citation

If you use AbsVis, please cite:

@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:

@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. EMNLP 2024.
Tater et al. (2024). Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts. CMCL 2024.
Tater et al. (2022). Concreteness vs. Abstractness: A Selectional Preference Perspective. AACL-IJCNLP 2022 SRW.

Contact

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