Instructions to use Intel/tvp-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/tvp-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, TvpForVideoGrounding processor = AutoProcessor.from_pretrained("Intel/tvp-base") model = TvpForVideoGrounding.from_pretrained("Intel/tvp-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# TVP base model
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The TVP model was proposed in [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/
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this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems. It was introduced in
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[this paper](https://arxiv.org/pdf/2303.04995.pdf).
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# TVP base model
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The TVP model was proposed in [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/2303.04995) by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding. The goal of
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this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems. It was introduced in
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[this paper](https://arxiv.org/pdf/2303.04995.pdf).
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