language: en
license: mit
tags:
- vision
- image-to-text
- video-to-text
- image-captioning
- video-captioning
- visual-question-answering
pipeline_tag: image-to-text
VideoBLIP, OPT-2.7b, fine-tuned on Ego4D
VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone.
Model description
VideoBLIP is an augmented BLIP-2 that can handle videos.
Bias, Risks, Limitations, and Ethical Considerations
VideoBLIP-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, OPT-175B has limitations in terms of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern large language models.
VideoBLIP has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
How to use
For code examples, please refer to the official repository.