Deepmind technologies limited (20240282094). MULTIMODAL FEW-SHOT LEARNING WITH FROZEN LANGUAGE MODELS simplified abstract
MULTIMODAL FEW-SHOT LEARNING WITH FROZEN LANGUAGE MODELS
Organization Name
Inventor(s)
Maria Rafailia Tsimpoukelli of London (GB)
Jacob Lee Menick of London (GB)
Felix George Hill of London (GB)
Seyed Mohammadali Eslami of London (GB)
MULTIMODAL FEW-SHOT LEARNING WITH FROZEN LANGUAGE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240282094 titled 'MULTIMODAL FEW-SHOT LEARNING WITH FROZEN LANGUAGE MODELS
Simplified Explanation
The patent application describes methods, systems, and apparatus for processing multi-modal inputs using language models. Specifically, the inputs include an image that is encoded by an image encoder neural network to generate a sequence of image embeddings. These embeddings are then used as part of an input sequence processed by a language model neural network.
Key Features and Innovation
- Processing multi-modal inputs using language models
- Encoding images with an image encoder neural network
- Generating image embeddings for input sequences
- Utilizing language model neural networks for processing
Potential Applications
This technology can be applied in various fields such as computer vision, natural language processing, and artificial intelligence research.
Problems Solved
This technology addresses the challenge of effectively processing multi-modal inputs, particularly images, using language models.
Benefits
- Improved accuracy in processing multi-modal inputs
- Enhanced capabilities in understanding and analyzing images
- Increased efficiency in language model processing
Commercial Applications
Title: Multi-Modal Input Processing Technology for Enhanced Image Analysis This technology can be utilized in industries such as healthcare, autonomous vehicles, and e-commerce for tasks like image recognition, content analysis, and data interpretation.
Prior Art
Readers can explore prior research in the fields of computer vision, natural language processing, and neural networks to understand the evolution of multi-modal input processing technologies.
Frequently Updated Research
Researchers are continuously exploring advancements in image encoding techniques, language model architectures, and multi-modal input processing algorithms to enhance the capabilities of this technology.
Questions about Multi-Modal Input Processing
1. How does this technology improve the accuracy of processing multi-modal inputs?
- This technology enhances accuracy by leveraging image embeddings generated by neural networks for more effective language model processing.
2. What are the potential applications of multi-modal input processing in real-world scenarios?
- This technology can be applied in various industries for tasks such as image recognition, content analysis, and data interpretation.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing multi-modal inputs using language models. in particular, the inputs include an image, and the image is encoded by an image encoder neural network to generate a sequence of image embeddings representing the image. the sequence of image embeddings is provided as at least part of an input sequence to that is processed by a language model neural network.
