Huawei technologies co., ltd. (20240135491). FEATURE MAP PROCESSING METHOD AND RELATED DEVICE simplified abstract
Contents
- 1 FEATURE MAP PROCESSING METHOD AND RELATED DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FEATURE MAP PROCESSING METHOD AND RELATED DEVICE - 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
FEATURE MAP PROCESSING METHOD AND RELATED DEVICE
Organization Name
Inventor(s)
FEATURE MAP PROCESSING METHOD AND RELATED DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135491 titled 'FEATURE MAP PROCESSING METHOD AND RELATED DEVICE
Simplified Explanation
The patent application describes a feature map processing method using a neural network model with input adaptation branches and a post-processing part.
- The method involves invoking a neural network model with multiple input adaptation branches.
- Each input adaptation branch can downsample a feature map at a different ratio.
- The output of each input adaptation branch serves as input to the post-processing part.
Potential Applications
This technology could be applied in image recognition, object detection, and natural language processing tasks.
Problems Solved
This method helps in improving the efficiency and accuracy of feature map processing in artificial intelligence applications.
Benefits
The method allows for more flexible and adaptive feature map processing, leading to better performance in various AI tasks.
Potential Commercial Applications
- Enhanced image recognition systems
- Improved natural language processing algorithms
Possible Prior Art
There may be prior art related to neural network models with input adaptation branches, but specific examples are not provided in the abstract.
Unanswered Questions
How does this method compare to existing feature map processing techniques in terms of computational efficiency?
The abstract does not mention how this method compares to other techniques in terms of computational efficiency.
Are there any limitations to the different downsampling ratios used by the input adaptation branches?
The abstract does not address any potential limitations or challenges related to using different downsampling ratios in the input adaptation branches.
Original Abstract Submitted
this application relates to the field of artificial intelligence technologies, and provides a feature map processing method and a related device. the method is implemented by invoking a neural network model that includes a plurality of input adaptation branches and a post-processing part. an output of each of the plurality of input adaptation branches is an input of the post-processing part, and each of the plurality of input adaptation branches can downsample a feature map at a different ratio.
