Datasets:
| license: mit | |
| task_categories: | |
| - video-classification | |
| tags: | |
| - temporal-reasoning | |
| - video-understanding | |
| - benchmark | |
| - vision-language | |
| dataset_info: | |
| features: | |
| - name: relative_path | |
| dtype: string | |
| - name: file | |
| struct: | |
| - name: bytes | |
| dtype: binary | |
| - name: path | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 3012200454 | |
| num_examples: 595 | |
| download_size: 3010737092 | |
| dataset_size: 3012200454 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| # SpookyBench: A Benchmark for Purely Temporal Video Understanding | |
| SpookyBench is a novel benchmark dataset designed to evaluate the ability of video-language models (VLMs) to understand purely temporal patterns, independent of spatial cues. The dataset consists of 451 videos across four categories: Text, Object Images, Dynamic Scenes, and Shapes. Each video appears as random noise in individual frames, but reveals meaningful content (words, objects, etc.) when viewed as a temporal sequence. This design exposes a critical limitation in current VLMs, which often heavily rely on spatial information and struggle to extract meaning from purely temporal sequences. | |
| [Paper: Time Blindness: Why Video-Language Models Can't See What Humans Can?](https://huggingface.co/papers/2505.24867) | |
| [Project Website: https://timeblindness.github.io/](https://timeblindness.github.io/) | |
| The dataset contains 451 videos distributed as follows: | |
| | **Category** | **Total Videos** | **Description** | | |
| |-------------|-----------------|----------------| | |
| | **Text** | 210 (46.6%) | English words encoded through temporal noise patterns | | |
| | **Object Images** | 156 (34.6%) | Single objects encoded using temporal animation | | |
| | **Dynamic Scenes** | 57 (12.6%) | Video depth maps with temporal motion patterns | | |
| | **Shapes** | 28 (6.2%) | Geometric patterns encoded through temporal sequences | | |
| | **Total** | **451** | **Comprehensive temporal understanding evaluation** | | |
| **Download:** You can download the dataset from Hugging Face using the following command: | |
| ```bash | |
| wget https://huggingface.co/datasets/timeblindness/spooky-bench/resolve/main/spooky_bench.zip | |
| unzip spooky_bench.zip | |
| ``` | |
| **License:** MIT License |