18527528. Multi-scale Transformer for Image Analysis simplified abstract (GOOGLE LLC)
Contents
- 1 Multi-scale Transformer for Image Analysis
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Multi-scale Transformer for Image Analysis - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Multi-scale Transformer for Image Analysis
Organization Name
Inventor(s)
Junjie Ke of East Palo Alto CA (US)
Feng Yang of Sunnyvale CA (US)
Qifei Wang of Mountain View CA (US)
Yilin Wang of Sunnyvale CA (US)
Peyman Milanfar of Menlo Park CA (US)
Multi-scale Transformer for Image Analysis - A simplified explanation of the abstract
This abstract first appeared for US patent application 18527528 titled 'Multi-scale Transformer for Image Analysis
Simplified Explanation
The technology described in the abstract is a patch-based multi-scale Transformer that can be used in various imaging applications. It allows for predicting image quality effectively on native resolution images by transforming them into a multi-scale representation. This enables the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches.
- Spatial embedding is used to map patch positions to a fixed grid, where patch locations at each scale are hashed to the same grid.
- Scale embedding is employed to distinguish patches coming from different scales in the multi-scale representation.
- Self-attention is performed to create a final image representation, with the option to prepend a learnable classification token to the set of input tokens before performing self-attention.
Potential Applications
This technology can be applied in various imaging applications such as image enhancement, object recognition, and medical imaging.
Problems Solved
1. Constraints on image fixed input size are avoided. 2. Predicting image quality effectively on native resolution images.
Benefits
1. Improved image quality prediction. 2. Flexibility in handling images of different resolutions. 3. Enhanced performance in various imaging applications.
Potential Commercial Applications
Optimized Image processing technology for industries such as healthcare, entertainment, and surveillance.
Possible Prior Art
There may be prior art related to image processing techniques using multi-scale representations and self-attention mechanisms in the field of computer vision and artificial intelligence.
Unanswered Questions
How does the technology handle real-time image processing applications?
The article does not provide information on the real-time processing capabilities of the technology.
What are the computational requirements for implementing this technology?
The article does not mention the computational resources needed to deploy the patch-based multi-scale Transformer.
Original Abstract Submitted
The technology employs a patch-based multi-scale Transformer () that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image () is transformed into a multi-scale representation (), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding () is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding () is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention () is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token () to the set of input tokens.
