| --- |
| language: |
| - en |
| pretty_name: CUHK-X — Large Model Track |
| tags: |
| - video |
| - multimodal |
| - human-activity |
| - depth |
| - infrared |
| - thermal |
| task_categories: |
| - visual-question-answering |
| --- |
| # CUHK-X — Large Model Track |
|
|
| Multimodal **video question answering (VQA)** over human daily-activity clips recorded at home. |
| Given a short multimodal video, answer multiple-choice questions about it. |
|
|
| ## Repository layout |
|
|
| ``` |
| . |
| ├── Training/ |
| │ ├── training_qa.csv # questions + answers |
| │ ├── modality_list.csv # which modalities each clip has |
| │ └── data/ |
| │ ├── HARn.zip → HARn/<action>/<user>/<trial>/<modality>/<modality>.mp4 |
| │ └── HAU.zip → HAU/<user>/<trial>/<modality>/<modality>.mp4 |
| └── Testing/ |
| ├── test_qa.csv # questions only (fill the `prediction` column) |
| └── data/ |
| └── large_model_track_test.zip → large_model_track_test/<id>/<modality>/<modality>.mp4 |
| ``` |
|
|
| The videos are packaged as the `*.zip` files under each `data/` folder — unzip them first. |
| **The `path` column in the CSVs is relative to the extracted root** (i.e. it starts with |
| `HARn/`, `HAU/`, or `large_model_track_test/`). |
|
|
| ### Two sources (`source` column) |
|
|
| - **HARn** — single-action clips; the path contains the action, e.g. `HARn/0_Wash_face/user16/1-1-2`. |
| - **HAU** — complex / multi-action clips; the path is `HAU/<user>/<trial>`. |
|
|
| Test clips are anonymized as `LM_test_XXXX`. |
|
|
| ### Modalities |
|
|
| Each clip is a directory with one sub-directory per modality, each holding a single `.mp4` (MPEG-4, 10 fps): |
|
|
| | Modality | Description | HARn | HAU | |
| | --------------- | ----------------------------------------------- | :--: | :-: | |
| | `Depth` | depth map (16-bit, normalized to 8-bit gray) | ✓ | ✓ | |
| | `Depth_Color` | colorized depth | ✓ | ✓ | |
| | `IR` | infrared | ✓ | ✓ | |
| | `Thermal` | thermal (25 fps; frame count differs by design) | — | ✓ | |
|
|
| Not every clip has every modality — see `modality_list.csv`. (HARn never includes Thermal.) |
|
|
| ## Files |
|
|
| **`Training/training_qa.csv`** — `qa_id, source, path, category, question, A, B, C, D, answer` |
| |
| - `path` is the **clip directory**, e.g. `HARn/0_Wash_face/user16/1-1-2`. |
| - Load a modality from `<path>/<modality>/<modality>.mp4`. |
| |
| **`Testing/test_qa.csv`** — same columns, but `path` is a **specific modality file**, |
| e.g. `large_model_track_test/LM_test_0066/Depth/Depth.mp4`. Swap the trailing `Depth/Depth.mp4` for |
| another modality if needed. There is no `answer`; fill the empty `prediction` column. |
|
|
| **`Training/modality_list.csv`** — `path, present_modalities, missing_modalities` (semicolon-separated). |
| |
| > Note: in **training**, `path` points to a clip *directory*; in **testing**, it points to a modality *file*. |
| |
| ### Question categories |
| |
| `single`, `multi`, `object_interaction`, `sequence`, `combination`, `emotion`. |
| |
| ## Submission |
| |
| For each `qa_id` in `test_qa.csv`, fill `prediction` with the option letter(s): a single letter (e.g. `A`) |
| for single-answer questions, or several (e.g. `AC`) for multi-answer questions. |
| |
| ## Statistics |
| |
| | | Clips | Questions | |
| | -------- | ----: | --------: | |
| | Training | 3,912 | 4,087 | |
| | Testing | 208 | 682 | |
| |
| ## Quick start |
| |
| ```python |
| import csv, os |
| |
| # 1) unzip Training/data/HARn.zip and HAU.zip into ROOT (you get HARn/ and HAU/) |
| ROOT = "Training/data" # wherever you extracted |
| |
| for r in csv.DictReader(open("Training/training_qa.csv", encoding="utf-8-sig")): |
| # training path is a clip directory -> pick a modality |
| depth_mp4 = os.path.join(ROOT, r["path"], "Depth", "Depth.mp4") |
| # use r["question"], r["A"]..r["D"], r["answer"] |
| ``` |
