Expand dataset card: full description, structure, uses, and citation of original paulgavrikov/visualoverload

README.md

@@ -1,46 +1,42 @@
 ---
-annotations_creators: []
-language: en
+annotations_creators:
+- expert-generated
+language:
+- en
 license: cc-by-sa-4.0
 size_categories:
 - 1K<n<10K
-task_categories: []
+task_categories:
+- visual-question-answering
+- image-text-to-text
 task_ids: []
 pretty_name: VisualOverload
 tags:
 - fiftyone
-- fiftyone
 - image
-- image
-- visual-question-answering
 - vqa
-description: VisualOverload (CVPR 2026) is a visual question answering (VQA) benchmark
-  of 2,720 question-answer pairs over 150 high-resolution public-domain paintings
-  densely populated with figures, actions, and unfolding subplots. It probes whether
-  VLMs can perform simple, knowledge-free vision tasks in overloaded scenes. Ground-truth
-  answers are held privately; models are scored via the official evaluation server
-  using each question's question_id. This is a FiftyOne-format version of paulgavrikov/visualoverload.
-dataset_summary: '
+- visual-question-answering
+- art
+dataset_summary: |
 
 
 
   ![image/png](dataset_preview.jpg)
 
 
-
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2720 samples.
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2720 samples,
+  converted to FiftyOne format from the original
+  [paulgavrikov/visualoverload](https://huggingface.co/datasets/paulgavrikov/visualoverload).
 
 
   ## Installation
 
 
-  If you haven''t already, install FiftyOne:
+  If you haven't already, install FiftyOne:
 
 
   ```bash
-
   pip install -U fiftyone
-
   ```
 
 
@@ -48,39 +44,28 @@ dataset_summary: '
 
 
   ```python
-
   import fiftyone as fo
-
   from fiftyone.utils.huggingface import load_from_hub
 
-
   # Load the dataset
-
-  # Note: other available arguments include ''max_samples'', etc
-
+  # Note: other available arguments include 'max_samples', etc
   dataset = load_from_hub("Voxel51/VisualOverload")
 
-
   # Launch the App
-
   session = fo.launch_app(dataset)
-
   ```
-
-  '
 ---
 
 # Dataset Card for VisualOverload
 
-<!-- Provide a quick summary of the dataset. -->
-
-
-
-
 ![image/png](dataset_preview.jpg)
 
-
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2720 samples.
+This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with **2,720 samples**.
+It is a FiftyOne-format conversion of the original
+[**paulgavrikov/visualoverload**](https://huggingface.co/datasets/paulgavrikov/visualoverload)
+dataset (CVPR 2026). All credit for the data, annotations, and benchmark design belongs to
+the original authors — please see [Citation](#citation) and
+[Dataset Sources](#dataset-sources).
 
 ## Installation
 
@@ -104,135 +89,159 @@ dataset = load_from_hub("Voxel51/VisualOverload")
 session = fo.launch_app(dataset)
 ```
 
-
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
+Is basic visual understanding really solved in state-of-the-art VLMs? **VisualOverload** is a
+visual question answering (VQA) benchmark comprising **2,720 question–answer pairs** with
+privately held ground-truth responses. Unlike prior VQA datasets that typically focus on
+near-global image understanding, VisualOverload challenges models to perform simple,
+knowledge-free vision tasks in densely populated (or *overloaded*) scenes. The dataset
+consists of **150 high-resolution scans of public-domain paintings** populated with multiple
+figures, actions, and unfolding subplots set against elaborately detailed backdrops. The
+images were manually annotated with questions across six task categories to probe a thorough
+understanding of the scene.
+
+The authors hypothesize that current benchmarks overestimate the performance of VLMs, and
+that encoding and reasoning over details remains challenging, especially in densely populated
+scenes. Indeed, even the best model evaluated (o3) out of 37 tested models reaches only
+**19.6% accuracy on the hardest split** and **69.5% overall**. The accompanying error
+analysis reveals failure modes including weak counting, OCR failures, and logical
+inconsistencies under complex tasks.
+
+- **Curated by:** Paul Gavrikov, Wei Lin, M. Jehanzeb Mirza, Soumya Jahagirdar, Muhammad Huzaifa, Sivan Doveh, Serena Yeung-Levy, James Glass, and Hilde Kuehne
+- **Shared by:** [Voxel51](https://huggingface.co/Voxel51) (FiftyOne-format conversion)
 - **Language(s) (NLP):** en
-- **License:** cc-by-sa-4.0
-
-### Dataset Sources [optional]
+- **License:** CC BY-SA 4.0 (the underlying images are royalty-free public-domain artwork, CC0)
 
-<!-- Provide the basic links for the dataset. -->
+### Dataset Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Original dataset (please cite this):** https://huggingface.co/datasets/paulgavrikov/visualoverload
+- **Repository:** https://github.com/paulgavrikov/visualoverload
+- **Paper:** [VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes (arXiv:2509.25339)](https://arxiv.org/abs/2509.25339)
+- **Project page:** https://paulgavrikov.github.io/visualoverload/
+- **Leaderboard / online evaluator:** https://huggingface.co/spaces/paulgavrikov/visualoverload-submit
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+- Benchmark the fine-grained visual understanding of vision-language models (VLMs) in dense,
+  detail-heavy scenes.
+- Slice and analyze results by **question type**, **difficulty**, and **category** using the
+  prefixed sample tags (see [Dataset Structure](#dataset-structure)).
+- Run a VLM per question — each sample carries a single `question` (and a ready-to-use
+  `default_prompt`), so a model can read the prompt from the sample field and write one
+  prediction per sample, then submit `question_id` + predicted answer to the official
+  evaluation server.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- **Training / fine-tuning.** This is an evaluation benchmark; ground-truth answers are held
+  privately and are intentionally not distributed.
+- Drawing conclusions about general image understanding outside the dense-scene,
+  painting-domain setting the benchmark was designed for.
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+The benchmark is modeled **one sample per question**: **2,720 samples** over **150 paintings**
+(each image is shared by the ~18 questions that reference it). Ground-truth answers are **not
+included** — models are scored via the official evaluation server using each question's
+`question_id`. All samples belong to the single `test` split.
 
-[More Information Needed]
+**Fields**
 
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
+| Field | Type | Description |
+|-------|------|-------------|
+| `filepath` | image | Path to the painting (shared across its questions) |
+| `question_id` | `StringField` | Unique id — the key used for leaderboard submissions |
+| `question` | `StringField` | The question about the image |
+| `response_options` | `ListField(StringField)` | Answer options for `choice` questions (e.g. `["yes", "no"]`); empty otherwise. Listed as `options` in the source dataset. |
+| `default_prompt` | `StringField` | Ready-to-use prompt (question + options + output-format constraint) |
+| `image_id` | `StringField` | Painting id (filename stem) — groups an image's questions |
+| `win_rate` | `FloatField` | Per-image model win-rate from the benchmark (a difficulty signal) |
+| `metadata` | `ImageMetadata` | Image width/height (most images are ~4K, e.g. 3840×2160) |
 
-[More Information Needed]
+**Sample tags** — `question_type`, `difficulty`, and `category` are stored as **prefixed
+sample tags** (filter via the App sidebar or `dataset.match_tags(...)`). They are prefixed
+because `question_type` and `category` share the values `counting` and `ocr`.
 
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
+| Tag prefix | Values (counts) |
+|------------|-----------------|
+| `question_type:` | `choice` (2043), `counting` (559), `ocr` (118) |
+| `difficulty:` | `easy` (986), `medium` (1304), `hard` (430) |
+| `category:` | `activity` (150), `attributes` (149), `counting` (559), `ocr` (118), `reasoning` (356), `scene` (1388) |
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+Every sample is also tagged `test`.
 
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
+```python
+# Example: all hard OCR questions
+from fiftyone import ViewField as F
+hard_ocr = dataset.match_tags(["difficulty:hard", "question_type:ocr"], all=True)
+```
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+## Dataset Creation
 
-[More Information Needed]
+### Curation Rationale
 
-#### Who are the annotators?
+Existing VQA benchmarks largely probe near-global image understanding and may overestimate
+VLM capability. VisualOverload deliberately targets *simple, knowledge-free* perception
+(reading, counting, attribute and activity recognition, scene/relationship reasoning) in
+**overloaded** scenes that contain many figures, actions, and subplots, to expose the gap in
+encoding and reasoning over fine detail.
 
-<!-- This section describes the people or systems who created the annotations. -->
+### Source Data
 
-[More Information Needed]
+#### Data Collection and Processing
 
-#### Personal and Sensitive Information
+The images are high-resolution scans of **public-domain paintings** (CC0). Most match a 4K
+pixel budget (≈ 3840×2160) across varying aspect ratios.
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+#### Annotations
 
-[More Information Needed]
+The images were **manually annotated** with questions spanning six task categories
+(`activity`, `attributes`, `counting`, `ocr`, `reasoning`, `scene`), three difficulty levels
+(`easy`, `medium`, `hard`), and three answer/question types (`choice` with 2 or 4 options,
+freeform `counting`, and freeform `ocr`). Ground-truth answers are withheld to prevent
+contamination and are only accessible through the evaluation server.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Evaluation-only:** ground truth is private; scoring requires the official server, so this
+  copy cannot be used for supervised training or offline scoring.
+- **Domain:** the imagery is limited to scanned public-domain paintings; performance here may
+  not transfer to photographs or other domains.
+- **Scale:** 150 source images / 2,720 questions — small relative to large-scale VQA corpora.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use VisualOverload as a targeted probe of fine-grained perception in dense scenes rather than
+a general VQA score. Report results by difficulty and category (the sample tags make this
+easy) and submit predictions to the official evaluator for comparable, leak-free numbers.
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+## Citation
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+If you use this dataset, please cite the original work:
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@InProceedings{Gavrikov_2026_visualoverload,
+  author    = {Paul Gavrikov and Wei Lin and M. Jehanzeb Mirza and Soumya Jahagirdar and Muhammad Huzaifa and Sivan Doveh and Serena Yeung-Levy and James Glass and Hilde Kuehne},
+  title     = {{VisualOverload}: Probing Visual Understanding of VLMs in Really Dense Scenes},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  month     = {June},
+  year      = {2026}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+Gavrikov, P., Lin, W., Mirza, M. J., Jahagirdar, S., Huzaifa, M., Doveh, S., Yeung-Levy, S., Glass, J., & Kuehne, H. (2026). *VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes.* In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
 
-## Dataset Card Contact
+## Dataset Card Authors
 
-[More Information Needed]
+FiftyOne-format conversion shared by [Voxel51](https://huggingface.co/Voxel51). The dataset,
+annotations, and benchmark were created by Paul Gavrikov et al.; see the original dataset at
+[paulgavrikov/visualoverload](https://huggingface.co/datasets/paulgavrikov/visualoverload).