README.md

metadata

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

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"]