CameraBench
The CameraBench dataset aims to better understand camera movements in videos. It includes more than 1,000 manually annotated clips, making it possible to evaluate the performance of generative and discriminatory models in a multimodal vision.
Description
CameraBench is a set of annotated videos for studying camera movements and evaluating multimodal models. It includes clips annotated with expert labels and captions to test the geometry and semantics captured by various models.
What is this dataset for?
- Evaluate the performance of multimodal vision models (VLMs) on video perception tasks
- Analyzing and understanding camera movements in video sequences
- Facilitate fine-tuning and improvement of multimodal models for video tasks
Can it be enriched or improved?
Yes, the dataset can be enriched by additional annotations or by adding new video clips to increase the diversity of movements and contexts. The fine-tuning of specific models is encouraged to improve multimodal perception.
🔎 In summary
🧠 Recommended for
- Video vision researchers
- VLM developers
- Video comprehension projects
🔧 Compatible tools
- PyTorch
- TensorFlow
- Multimodal frameworks
- Video annotation notebooks
💡 Tip
Use this dataset to compare the performances between generative and discriminatory models on visual perception.
Frequently Asked Questions
What is the size of the CameraBench dataset?
The dataset includes approximately 1,071 annotated video clips, with a very light total weight (87 KB in parquet format).
What type of tasks can you evaluate with CameraBench?
Mainly the tasks of analyzing camera movements and evaluating the capabilities of multimodal models on video.
What license does this dataset cover?
The dataset is under the MIT license, free to use, including commercial.
