Google llc (20240119555). Multi-scale Transformer for Image Analysis simplified abstract
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 20240119555 titled 'Multi-scale Transformer for Image Analysis
Simplified Explanation
The technology described in the patent application is a patch-based transformer that can be used in various image applications. It transforms native resolution images into a multi-scale representation, allowing the self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches.
- The technology employs a patch-based transformer
- It transforms native resolution images into a multi-scale representation
- Self-attention mechanism captures information on fine-grained and coarse-grained patches
- Spatial embedding maps patch positions to a fixed grid
- Scale embedding distinguishes patches from different scales
- Self-attention creates a final image representation
- Learnable classification token may be prepended to input tokens
Potential Applications
This technology can be applied in various image processing applications such as image recognition, object detection, and image enhancement.
Problems Solved
1. Constraints on image fixed input size are avoided 2. Quality can be effectively predicted on native resolution images
Benefits
1. Improved image quality prediction 2. Enhanced image processing capabilities 3. Flexibility in handling images of different sizes
Potential Commercial Applications
1. Image editing software 2. Surveillance systems 3. Medical imaging technology
Possible Prior Art
There may be prior art related to patch-based transformers in the field of image processing and artificial intelligence.
Unanswered Questions
How does this technology compare to existing image processing algorithms?
The article does not provide a direct comparison with other image processing algorithms in terms of performance, efficiency, or accuracy.
What are the potential limitations or drawbacks of this technology?
The article does not mention any potential limitations or drawbacks of the patch-based transformer technology.
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.
