QCRI/ImageEval2026-Task1

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

1. Load the subset you are targeting:

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

2. 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
     

3. 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.

📏 Evaluation

Each of the four tracks is scored separately. One ranking metric decides the leaderboard; the remaining columns are reported as diagnostics. A missing or unparseable prediction always counts as wrong.

Task 1a: Spoken VQA

A 3-way multiple choice: predict the option index 0, 1 or 2.

metric	role	meaning
Accuracy	ranking	fraction of items answered correctly
Balanced accuracy	reported	mean per-class recall over the three positions
Macro-F1	reported	macro-averaged F1 over the three positions

Task 1c: Hallucination detection

Three statements per image; predict True/False for each. Exactly one statement is grounded ("Q+"); the other two are hallucinated ("Q−").

metric	role	meaning
Combined accuracy	ranking	fraction of items where all three labels are correct (grounded → true, both hallucinated → false)
Hallucination rate	reported	q_plus_accuracy − combined_accuracy; lower is better
Conditional hallucination rate (CFHR-2)	reported	of items where the grounded statement was correctly identified, the fraction that still affirmed a hallucinated one; lower is better
CFHR-3	reported	of items with ≥1 of the three correct, the fraction not fully correct; lower is better
Q+ accuracy	reported	grounded statement correctly marked true
Q− accuracy	reported	hallucinated statements correctly marked false (over all false statements)
Q− both accuracy	reported	both hallucinated statements marked false

True/False is read from the prediction with the shared‑task evaluate_tf parser, which handles English and Arabic verdicts (e.g. true/false, صح/خطأ).

🧪 Baselines & example notebooks

Starter Colab notebooks that run end to end (download the data → run a model → write a Codabench-ready prediction.zip) are here:

Reference notebooks (Google Drive)

• Open-model baseline: Task 1a (Qwen2.5-Omni) and Task 1c (Qwen2.5-VL), runnable on a free Colab T4 (4-bit).
• Cascaded API example (Task 1a): Fanar Aura-STT (speech → text) → Oryx (image understanding); no GPU required.

These are references only; participants are free to use their own models, prompts, and configurations.

Reference baseline scores:

task	track	model	metric	score
1a	English	Qwen2.5-Omni	accuracy	0.6640
1a	MSA	Qwen2.5-Omni	accuracy	0.3980
1c	English	Qwen2.5-VL	combined accuracy	0.6840
1c	MSA	Qwen2.5-VL	combined accuracy	0.5080

📜 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