Datasets:

QCRI
/

ImageEval2026-Task1-AynVQA

license: cc-by-nc-4.0
language:
  - ar
  - en
pretty_name: 'ImageEval 2026, Task 1: Ayn-VQA'
tags:
  - multimodal
  - arabic
  - visual-question-answering
  - hallucination-detection
  - speech
  - culture
configs:
  - config_name: task1a_en
    data_files:
      - split: train
        path: task1a/train_en.jsonl
      - split: dev
        path: task1a/dev_en.jsonl
      - split: devtest
        path: task1a/devtest_en.jsonl
  - config_name: task1a_msa
    data_files:
      - split: train
        path: task1a/train_msa.jsonl
      - split: dev
        path: task1a/dev_msa.jsonl
      - split: devtest
        path: task1a/devtest_msa.jsonl
  - config_name: task1c_en
    data_files:
      - split: train
        path: task1c/train_en.jsonl
      - split: dev
        path: task1c/dev_en.jsonl
      - split: devtest
        path: task1c/devtest_en.jsonl
  - config_name: task1c_msa
    data_files:
      - split: train
        path: task1c/train_msa.jsonl
      - split: dev
        path: task1c/dev_msa.jsonl
      - split: devtest
        path: task1c/devtest_msa.jsonl

ImageEval 2026, Task 1: Ayn-VQA 👁️

Culturally grounded Arabic multimodal evaluation, part of the ImageEval 2026 Shared Task at ArabicNLP 2026. Ayn (عين, "eye") tests whether a model can read a culturally specific image, both from a spoken Arabic question and by telling grounded descriptions apart from plausible but hallucinated ones.

Each task is offered as two language tracks, English and Modern Standard Arabic (MSA), scored separately.

📝 Register

Please fill in the registration form — it lets the organisers keep track of participants and notify you about data releases, deadlines, and any updates.

🎯 Tasks

Task 1a, Spoken VQA. Given an image and the spoken question and options (audio), choose the correct option.

Prediction: the option index 0, 1 or 2.

Task 1c, Hallucination detection. Given an image and three statements, decide for each statement whether it is True (grounded in the image) or False (a hallucination). Exactly one statement is grounded.

Prediction: a True/False label per statement.

🗂️ Subsets

config	task	language	Codabench
`task1a_en`	Spoken VQA	English	compete
`task1a_msa`	Spoken VQA	MSA	compete
`task1c_en`	Hallucination	English	compete
`task1c_msa`	Hallucination	MSA	compete

The English and MSA tracks of a task are parallel: same images, same answers, and the questions are translations of each other.

🌍 Countries

The dataset spans 18 Arab countries:

Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, UAE, Yemen.

🔊 Audio

The Task 1a questions in train, dev and devtest are synthetically generated using voice cloning (TTS). The questions in the final blind test set will be human-recorded; expect a speaker/recording-condition shift between the dev-phase audio and the test audio.

📂 Files

images/<id>.jpg            one image per item, shared across tasks and languages
audio/<lang>/<id>.wav      spoken question and options (Task 1a)
task1a/<split>_<lang>.jsonl
task1c/<split>_<lang>.jsonl

Media is referenced by relative path keyed on id, so inputs join to files directly.

Fields

Task 1a (task1a/<split>_<lang>.jsonl):

field	type	description
`id`	str	item id
`image`	str	`images/<id>.jpg`
`audio`	str	`audio/<lang>/<id>.wav`, the spoken question and the three options (no text is given; listen and answer)
`label`	int	index (0–2) of the correct option

Task 1c (task1c/<split>_<lang>.jsonl):

field	type	description
`id`	str	item id
`image`	str	`images/<id>.jpg`
`statements`	list[str]	three statements, exactly one grounded
`labels`	list[bool]	truth value of each statement (one `true`)

train and dev additionally include country, category and subcategory. These and the labels are not provided in devtest, nor will they be provided in test.

📊 Splits

split	labels	items	use
`train`	yes	3000	training and fine-tuning
`dev`	yes	500	validation
`devtest`	no	500	pre-competition; submit to Codabench
`test`	no	1000	competition

The blind test set is released later for the final phase.

🗓️ Timeline

phase	window	submit on
Development	`2026-05-22 → 2026-07-19`	`devtest` — leaderboard live
Testing	`2026-07-20 → 2026-07-29`	`test` — blind, final ranking

Dates may shift — watch the website and the registration form for announcements.

🚀 Submitting

Load the subset you are targeting:

from datasets import load_dataset
ds = load_dataset("QCRI/ImageEval2026-Task1-AynVQA", "task1c_msa", split="devtest")

Produce predictions:
- Task 1a — for each item, predict an index 0, 1 or 2. Write a CSV with columns id,prediction:
```
id,prediction
1dcdf6da...,0
803ca9b8...,2
```
- Task 1c — for each item, predict True/False for each of the three statements. Write a CSV with columns id,statement_index,prediction (statement_index is 0–2; prediction is true or false):
```
id,statement_index,prediction
1dcdf6da...,0,false
1dcdf6da...,1,true
1dcdf6da...,2,false
```
Zip the CSV as prediction.zip and submit to the matching Codabench competition (links in the Subsets table above): task1a_en · task1a_msa · task1c_en · task1c_msa.

Metrics. Task 1a: accuracy. Task 1c: combined accuracy (all three statements correct, primary), with the hallucination rate and the True / False (Q+ / Q−) accuracies reported alongside.

📜 License and contact

CC BY-NC 4.0, research use only.

Website: https://imageeval2026.github.io/
Tasks repo: https://github.com/ImageEval2026/ImageEval2026-tasks
Contact: imageeval2026@gmail.com