README.md

metadata

task_categories:
  - text-generation
  - question-answering
language:
  - en
size_categories:
  - 1K<n<10K

TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs

Baiqi Li¹, Kangyi Zhao², Ce Zhang¹, Chancharik Mitra³, Jean de Dieu Nyandwi³, Gedas Bertasius¹

¹University of North Carolina at Chapel Hill   ²University of Pittsburgh   ³Carnegie Mellon University

🏠Home Page | 🤗HuggingFace | 📖Paper | 🖥️ Code

Setup

git clone https://github.com/Baiqi-Li/TimeBlind.git
cd TimeBlind
git clone https://huggingface.co/datasets/BaiqiL/TimeBlind

Data Format

Each sample in TimeBlind/data.jsonl contains:

• index: unique sample index (0, 1, 2, ...)
• video_path: path to video file (e.g., TimeBlind/videos/vid_00000_0.mp4)
• question: the question
• answer: the ground truth answer
• type: "yes_no" or "multiple_choice"

Evaluation

see evaluate.py in our github page for more details!

import json
from utils import _load_json_list, build_answers, get_scores, add_question_suffix

data = _load_json_list("TimeBlind/data.jsonl")
predictions = []

for sample in data:
    video_path = sample["video_path"]  
    question = add_question_suffix(sample["question"], sample["type"])
    
    # Replace with your model inference
    model_output = your_model(video_path, question)
    
    predictions.append({
        "index": sample["index"],
        "video_path": video_path,
        "question": question,
        "model_output": model_output,
    })

json.dump(predictions, open("predictions.json", "w"), indent=2)

answers = build_answers(predictions, data)
scores = get_scores(answers)
print(scores)  # {'Q_Acc': ..., 'V_Acc': ..., 'Acc': ..., 'I_Acc': ...}

Metrics

I-Acc serves as our primary metric.

• Acc: Binary VQA accuracy
• Q_Acc: Question accuracy
• V_Acc: Video accuracy
• I_Acc: Instance accuracy (the primary metric in our pape)

Copyright & Infringement Notice

The data provided in this benchmark is intended for academic research purposes only. We respect the intellectual property rights of the content creators.

If you believe that any content in this dataset infringes upon your rights, please contact us at [baiqili@cs.unc.edu] (or libaiqi123@gmail.com) and we will remove the relevant content immediately.