| --- |
| pretty_name: WearerTextBench |
| language: |
| - zh |
| - en |
| task_categories: |
| - visual-question-answering |
| tags: |
| - video |
| - egocentric |
| - scene-text |
| - ai-glasses |
| - wearer-centered |
| size_categories: |
| - 1K<n<10K |
| license: other |
| license_name: wearertext-research-use |
| license_link: https://huggingface.co/datasets/AI4Reading/WearerTextBench/blob/main/USE_POLICY.md |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: test/** |
| --- |
| |
| # WearerTextBench |
|
|
| **WearerText: Benchmarking Wearer-Centered Scene Text Understanding in AI-Glasses Videos** |
|
|
| [Project & code](https://github.com/AprilYapingZhang/WearerText) · |
| [Project page](https://aprilyapingzhang.github.io/WearerText/) |
|
|
| ## Release scope |
|
|
| This repository contains the **test split only: 107 videos and 1,391 QA pairs**, |
| with 107 examples for each of 13 tasks. Each video is stored once and referenced |
| by 13 metadata rows. Videos occupy 2,471,387,363 bytes. |
|
|
| The original files use MPEG-4 Part 2 video (`mp4v`), which is not supported by |
| all browsers. A separate `preview/` folder contains H.264 720p display copies for |
| the project homepage. These are **not evaluation inputs** and are excluded from |
| the configured test split. Original videos are retained byte-for-byte under |
| `test/`. Download originals to reproduce experiments; do not substitute previews. |
|
|
| The manuscript describes a full benchmark of 1,101 videos and 15,336 QA pairs. |
| Its training split (994 videos, 13,945 QA pairs) is **not included** in this release. |
| Do not infer that those training data are downloadable here. |
|
|
| ## Loading |
|
|
| Requires Python 3.9+ and `datasets>=4,<5`. |
|
|
| ```python |
| from datasets import load_dataset, Video |
| |
| ds = load_dataset("AI4Reading/WearerTextBench", split="test") |
| # No video decoder needed when only reading paths and annotations. |
| ds = ds.cast_column("video", Video(decode=False)) |
| print(ds[0]) |
| ``` |
|
|
| For local validation before upload: |
|
|
| ```python |
| ds = load_dataset("videofolder", data_dir="/absolute/path/to/huggingface", |
| split="test") |
| ds = ds.cast_column("video", Video(decode=False)) |
| assert len(ds) == 1391 |
| ``` |
|
|
| Decoding frames through `datasets.Video` additionally requires a compatible |
| TorchCodec/PyTorch/FFmpeg installation. Alternatively open the MP4 files using |
| your preferred video reader. The plain seven-field annotation list is also |
| available as `annotations/test.json`. |
|
|
| ## Schema |
|
|
| One example represents one question, not one distinct video. |
|
|
| | Field | Meaning | |
| | --- | --- | |
| | video | Hugging Face Video feature; shared MP4 reference | |
| | video_path | Original MP4 basename | |
| | question_id | Unique corrected ID: `{level}_{task_id}_{original_suffix}` | |
| | task_id | One of the 13 final task IDs below | |
| | task_name | Full task name and acronym | |
| | level | Integer 1, 2 or 3 | |
| | question | Original-language wearer-centered question | |
| | answer | Verified reference answer | |
|
|
| `test/metadata.jsonl` contains these annotation fields plus `file_name`, the |
| VideoFolder join key; the loader converts it to the `video` feature. |
| No model predictions or judge outputs are bundled as ground truth. |
|
|
| ## Task hierarchy |
|
|
| | Level | ID | Task | |
| | --- | --- | --- | |
| | Text Perception | L1.1 | Dynamic Text Recognition (DTR) | |
| | Text Perception | L1.2 | Dynamic Text Localization (DTL) | |
| | Contextual Understanding | L2.1 | Query-Guided Text Extraction (QTE) | |
| | Contextual Understanding | L2.2 | Confirmation (CF) | |
| | Contextual Understanding | L2.3 | Cross-Lingual Understanding (CLU) | |
| | Contextual Understanding | L2.4 | Situated Semantic Interpretation (SSI) | |
| | Contextual Understanding | L2.5 | Wearer-Centered Situational Inference (WSI) | |
| | Wearer-Grounded Reasoning | L3.1 | History-Conditioned Intent Inference (HII) | |
| | Wearer-Grounded Reasoning | L3.2 | Action-Trajectory Intent Prediction (AIP) | |
| | Wearer-Grounded Reasoning | L3.3 | Structured Text Synthesis (STS) | |
| | Wearer-Grounded Reasoning | L3.4 | Spatio-Temporal Navigation Reasoning (STN) | |
| | Wearer-Grounded Reasoning | L3.5 | Goal-Oriented Comparison (GOC) | |
| | Wearer-Grounded Reasoning | L3.6 | Viewpoint Trajectory Description (VTD) | |
|
|
| **Legacy compatibility:** old L2.5 → current L3.3; old L3.3 → current L2.5. |
| Update the level prefix too, preserving the entire original suffix. The data |
| here are **already corrected**. Never apply that swap twice. |
|
|
| ## Collection and verification |
|
|
| The manuscript describes real-world RayNeo AI-glasses recordings in shopping, |
| transportation and tourist scenarios. It uses layout-aware reference text, |
| hybrid question generation, and multi-agent plus human verification (MAH-V). |
| The eight recording volunteers were members of the research team and provided |
| informed consent. The manuscript reports anonymization of third-party faces. |
|
|
| These statements describe the authors' documented process, not an independent |
| privacy audit of every frame in this upload package. The release owner must |
| confirm that the packaged files are the approved anonymized release copies. |
|
|
| ## Evaluation |
|
|
| Use the project's VerEval implementation with ground-truth annotations and model |
| predictions. Final score: **0.9 × Consistency + 0.1 × Logic**. Fact is an |
| intermediate diagnostic, not a third equally weighted component. |
| Report judge model/version, evidence mode, video-frame sampling, prompt version |
| and full test coverage. The paper's main table uses **evidence-based mode** |
| (question, prediction, verified ground truth; no video frames) with a local |
| Qwen3-VL-32B judge, temperature 0, a 1,024-token cap and a deterministic seed. |
| Video-aware judging is an optional alternative and must be reported separately. |
|
|
| ## Intended use and limitations |
|
|
| Academic, non-commercial research, with attribution and responsible handling; |
| see [use policy](USE_POLICY.md). No de-anonymization or identification attempts. |
| Public-scene and language coverage are bounded by the authors' recording |
| locations and device. Camera-view trajectory is not eye gaze. Short-clip offline |
| QA does not establish reliable long-horizon navigation or deployment safety. |
|
|
| ## Integrity and provenance |
|
|
| `manifest.json` records the bytes and SHA-256 of every video and metadata file. |
| `preview-manifest.json` separately records browser previews and source hashes. |
| `release-summary.json` records exact released counts. The package is derived |
| from the supplied corrected test annotations, without rewriting questions or |
| answers. The code repository retains the source task definitions and results |
| table for provenance. |
|
|
| The project-manuscript citation is available in the code repository at |
| `data/citation.bib`. No archival venue identifier or DOI is asserted by this |
| release card. |
|
|
