Initial release of AbsVis (EMNLP 2025): images with per-image CC attribution, annotations, nouns

LICENSE.md

@@ -0,0 +1,38 @@
+# AbsVis License
+
+AbsVis combines two kinds of content with different licenses.
+
+## 1. Annotations, nouns and metadata: CC BY 4.0
+
+All non-image content in this repository is licensed under the
+Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
+This covers human and model concept–explanation annotations, noun lists and concreteness categories, and
+image–noun mappings. Copyright © 2025 Tarun Tater, Diego Frassinelli, Sabine Schulte im Walde.
+
+## 2. Images: original per-image Creative Commons licenses
+
+The image files are unmodified copies of photographs from the YFCC100M dataset (Thomee et al., 2016),
+originally published on Flickr. **They are not licensed by the AbsVis authors.** Each image remains
+under the Creative Commons license chosen by its photographer, as recorded per image in the `images`
+config:
+
+- `license_name` / `license_url`: the image's license
+- `flickr_owner_name` / `flickr_owner_id`: the photographer
+- `flickr_page_url`: the original Flickr page
+
+Only these licenses occur:
+
+| License | URL |
+|---|---|
+| Attribution (CC BY 2.0) | https://creativecommons.org/licenses/by/2.0/ |
+| Attribution-ShareAlike (CC BY-SA 2.0) | https://creativecommons.org/licenses/by-sa/2.0/ |
+| Attribution-NonCommercial (CC BY-NC 2.0) | https://creativecommons.org/licenses/by-nc/2.0/ |
+| Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 2.0) | https://creativecommons.org/licenses/by-nc-sa/2.0/ |
+
+If you reuse an image, follow the terms of its license. That means crediting the photographer with
+a link to the Flickr page and license, and, for NonCommercial images, not using it commercially.
+Because the image set includes NonCommercial images, the full set may not be used for commercial
+purposes.
+
+If you are a photographer whose image appears here and would like it removed, please contact
+tarun.tater@ims.uni-stuttgart.de.

README.md

@@ -0,0 +1,171 @@
+---
+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 by the authors
+
+- 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

data/annotations.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d0a7dfdb9fda0f8b2a6dd983a02d98841abd81f191a00ddc81011c78c779501
+size 804206

data/images.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d601838e144a2eaaade1a373eaba28b131b0093bc0387bba0df73298f16e2c0
+size 88204607

data/nouns.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25f5f1218cc40695f8736704d98c783917daa0b4c8f29e285f6ed0fe082e2c65
+size 4802