Add dataset card

README.md

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+---
+license: mit
+task_categories:
+- image-text-to-text
+language:
+- en
+tags:
+- gui
+- agent
+- gui-agent
+- video
+- video-guided
+- benchmark
+- mobile
+- eccv2026
+pretty_name: VG-GUI-Bench
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: ours_data.json
+---
+
+# VG-GUI-Bench
+
+**Video-Guided GUI Agent Benchmark** — the benchmark contribution of our ECCV 2026 paper
+**[Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction](https://arxiv.org/abs/2606.29445)**.
+
+[![arXiv](https://img.shields.io/badge/arXiv-2606.29445-B31B1B.svg?logo=arxiv)](https://arxiv.org/abs/2606.29445)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue.svg?logo=github)](https://vg-gui-tasker.github.io/)
+[![Code](https://img.shields.io/badge/Code-GitHub-181717.svg?logo=github)](https://github.com/VG-GUI-TASKER/VG-GUI-TASKER)
+
+> **Authors.** Sunqi Fan, Qingle Liu, Runqi Yin, Meng-Hao Guo, Shuojin Yang (Tsinghua University).
+
+## What is VG-GUI-Bench?
+
+Recent Multimodal Large Language Models (MLLMs) achieve strong results on Video Question Answering
+(VideoQA), but existing benchmarks mostly probe *shallow visual perception* and rarely test whether a
+model can **learn a procedure from a tutorial video and carry it out** as a long-horizon interactive
+task. VG-GUI-Bench closes this gap.
+
+Given a **YouTube tutorial video** that demonstrates how to accomplish a task on a mobile device
+(e.g. *"How To Change Discord Password"*), a GUI agent must:
+
+1. **Understand the workflow** from reference frames drawn from the tutorial video (scene keyframes,
+   uniform samples, annotated frames, or algorithmically searched keyframes);
+2. **Localize the current progress** from its previous action history and the current screen; and
+3. **Predict the exact next action** — one of `CLICK`, `SCROLL`, `TYPE`, `PRESS`, `ZOOM`, `FINISH` —
+   on the current screen.
+
+This makes VG-GUI-Bench a testbed for **video in-context learning**: transferring procedural knowledge
+from an instructional video to grounded, step-by-step decision making.
+
+This dataset is a **processed and re-annotated version built on top of the
+[MONDAY](https://huggingface.co/datasets/runamu/MONDAY) dataset**, curated and packaged for the
+video-guided GUI agent evaluation described in the paper.
+
+## Dataset Statistics
+
+| Item | Value |
+|------|-------|
+| Episodes (tutorial videos / tasks) | 100 |
+| Total annotated steps | 1,071 |
+| Average steps per episode | 10.7 |
+| Source videos | 98 mobile-app tutorial videos (YouTube) |
+| Reference-frame variants per episode | 8 (see below) |
+| Total size | ~3 GB |
+
+### Action type distribution
+
+| `action_type_id` | `action_type_text` | Count |
+|:---:|---|---:|
+| 4 | `click` | 1,934 |
+| 4 | `scroll down` | 175 |
+| 4 | `scroll up` | 27 |
+| 4 | `scroll right` | 13 |
+| 4 | `scroll left` | 12 |
+| 3 | `type` | 50 |
+| 6 | `press home` | 39 |
+| 5 | `press back` | 36 |
+| 12 | `zoom or multi-touch` | 12 |
+| 14 | `other hardware` | 11 |
+
+## Repository Layout
+
+```
+VG-GUI-Bench/
+├── ours_data.json                 # Step-level action annotations (main label file)
+├── ytb_video/                     # Source tutorial videos, one .mp4 per episode (keyed by ep_id)
+│   ├── 07hF8RAFgIc.mp4
+│   └── ...
+└── images/                        # Pre-rendered frames, per reference mode → per episode → frames
+    ├── origin/                    #   Scene-timestamp keyframes (cropped to phone screen)
+    ├── origin_no_cut/             #   Scene-timestamp keyframes (full frame)
+    ├── annotation/                #   Frames with a red bounding box on the target (cropped)
+    ├── annotation_no_cut/         #   Frames with a red bounding box on the target (full frame)
+    ├── uniform_5/                 #   5 uniformly sampled frames (cropped)
+    ├── uniform_5_no_cut/          #   5 uniformly sampled frames (full frame)
+    ├── uniform_10/                #   10 uniformly sampled frames (cropped)
+    └── uniform_10_no_cut/         #   10 uniformly sampled frames (full frame)
+```
+
+Inside every `images/<mode>/` directory the frames are grouped by episode id, e.g.
+`images/origin/07hF8RAFgIc/frame_0000.png`, `frame_0001.png`, …
+
+## Data Schema
+
+`ours_data.json` is a single JSON object with one key, `"ours"`, whose value is a **list of 100
+episodes**. Each **episode** is a **list of steps**, and each **step** has the following fields:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `ep_id` | `str` | YouTube video id; also the key linking to `ytb_video/<ep_id>.mp4` and `images/<mode>/<ep_id>/`. |
+| `goal` | `str` | Natural-language task goal (the tutorial video title). |
+| `img_filename` | `str` | Relative frame path without extension, `"<ep_id>/frame_XXXX"`; the current screen for this step. |
+| `action_list` | `list[Action]` | One or more ground-truth action candidates for this step (see below). |
+
+Each **`Action`** object:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `action_type_id` | `int` | Numeric action type (see the distribution table above). |
+| `action_type_text` | `str` | Human-readable action type, e.g. `click`, `scroll down`, `type`, `press back`, `press home`, `zoom or multi-touch`. |
+| `annot_position` | `list[float]` | Normalized bounding box(es) of the target UI element, as a flat list of `[x, y, w, h]` quadruples (0.0–1.0). Multiple quadruples may be concatenated when several equivalent targets are annotated. |
+| `touch` | `[float, float]` | Normalized `(x, y)` gesture start point (0.0–1.0). |
+| `lift` | `[float, float]` | Normalized `(x, y)` gesture end point (0.0–1.0). For `click` this equals `touch`; for scrolls it differs. |
+| `type_text` | `str` | The text to enter for `type` actions; empty string otherwise. |
+
+All coordinates are **normalized to `[0, 1]`** relative to the screen width/height.
+
+### Minimal example
+
+```json
+{
+  "ep_id": "SIjOxM9jVj8",
+  "goal": "How To Change Discord Password 2021 | Discord Mobile App",
+  "img_filename": "SIjOxM9jVj8/frame_0000",
+  "action_list": [
+    {
+      "action_type_id": 4,
+      "action_type_text": "click",
+      "annot_position": [0.862, 0.079, 0.062, 0.116],
+      "touch": [0.137, 0.893],
+      "lift":  [0.137, 0.893],
+      "type_text": ""
+    }
+  ]
+}
+```
+
+## Reference-Frame Modes
+
+The benchmark studies how different visual-context strategies affect agent performance. The pre-rendered
+`images/` directories cover **8** of these modes (`origin`, `annotation`, `uniform_5`, `uniform_10`, each
+in `cut` / `no_cut` variants). The evaluation code additionally supports algorithmic keyframe-search modes
+(`tasker`, `bfs`, `gbfs`, `dijkstra`) and video-agent modes (`videoagent`, `videotree`), which are produced
+on demand from the source videos — see the [code repository](https://github.com/VG-GUI-TASKER/VG-GUI-TASKER).
+
+## Usage
+
+```python
+import json
+from huggingface_hub import snapshot_download
+
+local_dir = snapshot_download(repo_id="Aoraku/VG-GUI-Bench", repo_type="dataset")
+
+data = json.load(open(f"{local_dir}/ours_data.json"))["ours"]
+print(len(data), "episodes")
+for step in data[0]:                     # iterate over one episode
+    print(step["img_filename"], step["action_list"][0]["action_type_text"])
+```
+
+For the full data-processing pipeline, reference-mode generation, and the four evaluation metrics
+(Accuracy, Completion, Efficiency, PIR), see the official code:
+**https://github.com/VG-GUI-TASKER/VG-GUI-TASKER** (`VG-GUI-Bench/`).
+
+## License
+
+Released under the [MIT License](https://lbesson.mit-license.org/). The underlying videos remain the
+property of their respective YouTube uploaders and are provided for research use only.
+
+## Citation
+
+```bibtex
+@inproceedings{fan2026bridging,
+  title     = {Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction},
+  author    = {Fan, Sunqi and Liu, Qingle and Yin, Runqi and Guo, Meng-Hao and Yang, Shuojin},
+  booktitle = {European Conference on Computer Vision (ECCV)},
+  year      = {2026}
+}
+```
+
+## Acknowledgements
+
+Built on top of the [MONDAY](https://huggingface.co/datasets/runamu/MONDAY) dataset. We thank its authors
+for releasing the original data.