OneLLM
One Framework to Align All Modalities with Language
About OneLLM
OneLLM is a framework presented in the paper titled 'OneLLM: One Framework to Align All Modalities with Language'. It is designed to align multiple modalities with language using a unified framework. The framework consists of modality tokenizers, a universal encoder, a universal projection module (UPM), and a language-like model (LLM). By leveraging these components, OneLLM enables the alignment of various modalities such as images, videos, depth/normal maps, etc., with textual information. It is built on the idea of multi-modal language modeling and aims to integrate different modalities for tasks like image captioning, visual question answering, and more.
Key Features
- Modality tokenizers for transforming input signals into tokens.
- Universal encoder for representing input modalities in a common embedding space.
- Universal projection module (UPM) for mapping the modalities to the language space.
- Language-like model (LLM) for generating textual descriptions from the aligned modalities.
Use Cases
- Image captioning: Generating captions for images.
- Visual question answering: Answering questions based on visual content.
- Multi-modal sentiment analysis: Analyzing sentiments from combined textual and visual information.
- Cross-modal retrieval: Retrieving relevant information across different modalities.
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