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Datasets:
mukul54
/
spookybench-v2

Tasks:
Video Classification
Video-Text-to-Text
Zero-Shot Classification
Modalities:
Tabular
Text
Video
Size:
1K - 10K
ArXiv:
Tags:
video
noise
benchmark
vision-language
temporal-understanding
Libraries:
Datasets
License:
Dataset card Data Studio Files
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Upload SpookyBench-v2 dataset with static and dynamic videos

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README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - video-classification
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+ - video-text-to-text
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+ - zero-shot-classification
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+ pretty_name: SpookyBench-v2
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+ tags:
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+ - video
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+ - noise
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+ - speckle
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+ - benchmark
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+ - vision-language
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+ - temporal-understanding
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+
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+ # SpookyBench-v2
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+
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+ ## Dataset Description
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+
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+ SpookyBench-v2 is the updated version of [spooky-bench](https://huggingface.co/datasets/timeblindness/spooky-bench/) benchmark, designed to evaluate vision-language models' capabilities for temporal understanding. The dataset contains both static and dynamic videos with various noise densities and speckle sizes, specifically designed to test models' temporal perception abilities. Check our paper for more details.
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+
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+ ## Key Features
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+
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+ - Human accuracy around 98% while all of the models including closed source one gets
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+ - **Noise Robustness Testing**: Videos with varying noise densities (10-90) and speckle sizes
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+ - **Temporal Understanding**: Mix of static and dynamic content to test temporal perception
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+ - **Diverse Categories**: Images, words, shapes, real-world videos, and dual-object scenarios
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+ - **Movement Type Classification**: Explicit labeling of static vs. dynamic content
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+
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+ ## Dataset Statistics
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+
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+ - **Total Videos**: 1,127
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+ - **Static Frame Videos**: 394
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+ - **Dynamic Videos**: 733
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+
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+ ### Categories Breakdown
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+ - **dual_images**: 108 videos
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+ - **images**: 404 videos
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+ - **shapes**: 28 videos
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+ - **videos**: 57 videos
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+ - **words**: 530 videos
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+
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+ ## Dataset Structure
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+
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+ SpookyBenchDatasets/
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+ ├── metadata.csv # Dataset metadata with all annotations
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+ ├── images/ # Static single-object videos
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+ ├── words/ # Static word videos
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+ ├── shapes/ # Static geometric shape videos
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+ ├── videos/ # Dynamic real-world depth-map-based videos
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+ └── dynamic/ # Dynamic synthetic motion videos
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+ ├── images/ # Single object with dynamic motion
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+ ├── words/ # Words with dynamic motion
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+ └── dual_images/ # Two objects with dynamic motion
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+
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+
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+ ## Metadata Schema
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+
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+ The `metadata.csv` file contains the following columns:
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+
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+ | Column | Description |
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+ |--------|-------------|
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+ | `file_name` / `Path` | Relative path to video file |
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+ | `Category` | Video category (images/words/shapes/videos/dual_images) |
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+ | `Label` | Object/word labels in the video (list format) |
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+ | `Noise Density` | Noise level from 10 to 90 |
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+ | `Speckle Size` | Speckle size parameter (1, 3, etc.) |
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+ | `Frame Count` | Total number of frames |
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+ | `FPS` | Frames per second |
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+ | `Duration` | Video duration in seconds |
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+ | `Movement Type` | `static` or `dynamic` |
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+
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+ ## Usage
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+
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+ ### Loading the Dataset
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the entire dataset
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+ dataset = load_dataset("mukul54/spookybench-v2")
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+
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+ # Access a video
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+ example = dataset["train"][0]
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+ video_path = example["file_name"]
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+ label = example["Label"]
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+ noise_level = example["Noise Density"]
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+ movement = example["Movement Type"]
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+
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+ print(f"Video: {video_path}")
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+ print(f"Label: {label}")
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+ print(f"Noise: {noise_level}, Movement: {movement}")
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+ Filtering by Category
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+ # Filter static images only
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+ static_images = dataset.filter(
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+ lambda x: x["Category"] == "images" and x["Movement Type"] == "static"
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+ )
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+
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+ # Filter dynamic videos only
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+ dynamic_videos = dataset.filter(
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+ lambda x: x["Movement Type"] == "dynamic"
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+ )
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+
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+ # Filter high noise videos
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+ high_noise = dataset.filter(
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+ lambda x: x["Noise Density"] >= 70
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+ )
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+ Example Use Cases
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+ Noise Robustness Evaluation: Test model performance across different noise levels
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+ Temporal Understanding: Compare model accuracy on static vs dynamic content
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+ Zero-Shot Classification: Evaluate on diverse object categories
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+ Video-Language Alignment: Test understanding of noisy visual-text pairs
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+ Research Context
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+ This dataset is part of the research on temporal understanding in vision-language models, investigating why some models struggle with temporal perception that humans find trivial.
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+
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+ Related Work
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+ The dataset was created as part of research exploring "time blindness" in video-language models - the phenomenon where models fail to perceive temporal changes that are obvious to humans.
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+
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+ License
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+ MIT License - Free to use for research and commercial purposes.
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+
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+ Citation
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+ If you use this dataset in your research, please cite:
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+
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+ @article{upadhyay2025time,
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+ title={Time Blindness: Why Video-Language Models Can't See What Humans Can?},
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+ author={Upadhyay, Ujjwal and Ranjan, Mukul and Shen, Zhiqiang and Elhoseiny, Mohamed},
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+ journal={arXiv preprint arXiv:2505.24867},
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+ year={2025}
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+ }
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+ Contact
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+ For questions or issues regarding the dataset, please open an issue on the dataset repository or contact the authors.
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