TITLE = """<h1 align="center" id="space-title">Unofficial AirLetters Leaderboard</h1>"""

INTRODUCTION_TEXT = """
We introduce a new real-world dataset, utilizing human generated articulated motions with videos of people drawing Latin characters and labels. Unlike existing video datasets, accurate video understanding on our dataset requires detailed understanding of motion in the video and the integration of long-range information across the entire video. We show that existing image and video understanding models perform poorly and fall far behind the human baseline.

Unlike many video dataset which are overly dpeendent on one key frame or some which are highly dpeendent on 2-4 key frames, AirLetters require strong temporal capabilities. Our study revealed that while trivial for humans, accurate representations of complex articulated motions remain an open problem for end-to-end learning for models.

See:

- [The Paper](https://arxiv.org/abs/2410.02921)
- [Dataset Download Link](https://www.qualcomm.com/developer/software/airletters-dataset)
"""

LLM_BENCHMARKS_TEXT = INTRODUCTION_TEXT

EVALUATION_QUEUE_TEXT = """
1. Prepare your results in CSV format with columns: filename and label
2. Fill in your model details and URL
3. Upload your results file
4. Your model will be evaluated against the test set and added to the leaderboard automatically.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@inproceedings{dagliairletters,
    title={AirLetters: An Open Video Dataset of Characters Drawn in the Air},
    author={Dagli, Rishit and Berger, Guillaume and Materzynska, Joanna and Bax, Ingo and Memisevic, Roland},
    booktitle={European Conference on Computer Vision Workshops},
    year={2024}
}
"""