README.md

---
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](https://imageeval2026.github.io/) 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](https://docs.google.com/forms/d/e/1FAIpQLSd1QKF4rXD_gbLJlDykLvB0DGMIogwhraeOtWRiQiotucK0zA/viewform)**
— 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](https://www.codabench.org/competitions/16955/) |
| `task1a_msa` | Spoken VQA | MSA | [compete](https://www.codabench.org/competitions/16956/) |
| `task1c_en`  | Hallucination | English | [compete](https://www.codabench.org/competitions/16957/) |
| `task1c_msa` | Hallucination | MSA | [compete](https://www.codabench.org/competitions/16958/) |

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](https://imageeval2026.github.io/) and the
[registration form](https://docs.google.com/forms/d/e/1FAIpQLSd1QKF4rXD_gbLJlDykLvB0DGMIogwhraeOtWRiQiotucK0zA/viewform)
for announcements.

## 🚀 Submitting

1. Load the subset you are targeting:
   ```python
   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](https://www.codabench.org/competitions/16955/) ·
   [task1a_msa](https://www.codabench.org/competitions/16956/) ·
   [task1c_en](https://www.codabench.org/competitions/16957/) ·
   [task1c_msa](https://www.codabench.org/competitions/16958/).

**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