Tarsier: Recipes for Training and Evaluating Large Video Description Models
Paper • 2407.00634 • Published
Tarsier Model Card
Model details
Model type: Tarsier-34b is an open-source large-scale video-language models, which is designed to generate high-quality video descriptions, together with good capability of general video understanding (SOTA results on 6 open benchmarks).
Model date: Tarsier-34b was trained in June 2024.
Paper or resources for more information:
- github repo: https://github.com/bytedance/tarsier
- paper link: https://arxiv.org/abs/2407.00634
License
NousResearch/Nous-Hermes-2-Yi-34B license.
Where to send questions or comments about the model: https://github.com/bytedance/tarsier/issues
Intended use
Primary intended uses: The primary use of Tarsier is research on large multimodal models, especially video description.
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Training dataset
Tarsier tasks a two-stage training strategy.
- Stage-1: Multi-task Pre-training on 13M data
- Stage-2: Multi-grained Instruction Tuning on 500K data
In both stages, we freeze ViT and train all the parameters of projection layer and LLM.
Evaluation dataset
- A challenging video desription dataset: DREAM-1K
- Multi-choice VQA: MVBench, NeXT-QA and Egoschema
- Open-ended VQA: MSVD-QA, MSR-VTT-QA, ActivityNet-QA and TGIF-QA
- Video Caption: MSVD-Caption, MSRVTT-Caption, VATEX
How to Use
see https://github.com/bytedance/tarsier?tab=readme-ov-file#usage
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