Vista-LLaMA
The equidistant relationship between visual token and verbal token is utilized to achieve reliable video narration.
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Introduce：
Vista-LLaMA is an advanced video language model designed to improve video understanding. It reduces text generation unrelated to video content by maintaining a consistent distance between visual tokens and verbal tokens, regardless of the length of the generated text. This method omits the relative position encoding when calculating the weight of attention between visual and text tokens, making the influence of visual tokens in the text generation process more significant. Vista-LLaMA also introduces a sequential visual projector capable of projecting the current video frame into a token in the language space, capturing temporal relationships within the video while reducing the need for visual tokens. In a number of open video question-answering benchmark tests, the model significantly outperforms other methods.
https://pic.chinaz.com/ai/2024/01/24010805174072211821.jpg
Stakeholders:
For researchers and developers who need in-depth video content understanding and analysis.
Usage Scenario Examples:
- Researchers use Vista-LLaMA for deep understanding and analysis of complex video content.
- Developers use Vista-LLaMA to improve the accuracy of answers in a video question-and-answer system.
- Content creators use Vista-LLaMA to generate innovative video content.
The features of the tool:
- Maintain an equidistant relationship between visual tokens and linguistic tokens
- Reduce text generation unrelated to video content
- Sequential visual projectors capture temporal relationships within video
Tool’s Tabs: Video creation,AI animation production
