README.md

metadata

language:
  - en
task_categories:
  - video-text-to-text

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

Introduction

Fine-grained spatio-temporal understanding is essential for video reasoning and embodied AI. TimeBlind is a diagnostic benchmark for compositional spatio-temporal understanding. Inspired by cognitive science, TimeBlind categorizes fine-grained temporal understanding into three levels: recognizing atomic events, characterizing event properties, and reasoning about event interdependencies. It leverages a minimal-pairs paradigm where video pairs share identical static visual content but differ solely in temporal structure, utilizing complementary questions to neutralize language priors.

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 paper)

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@unc.cs.edu] and we will remove the relevant content immediately.