Global Feature Map Attention Fusion for Accurate Image Identification
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Solution Overview
Problem
Existing image processing technologies fail to effectively improve the quality of image features extracted from to-be-identified images, resulting in insufficient accuracy of computer vision identification functions such as face recognition, pedestrian recognition, and object recognition.
Innovation Solution
Introduce a channel attention mechanism to fuse low-order and high-order image information during visual identification, using a target channel attention model to extract low-order and high-order image information through low-order and high-order information learning sub-models, and perform attention vector fusion to enhance feature map quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to extract image features, then the processing speed is maintained, but the quality and accuracy of extracted image features are insufficient
Solution Approach 1:
The patent segments the image processing task into multiple processing stages with different network depths. Shallow networks process simple regions while deeper networks handle complex regions, allowing feature extraction quality to be improved where needed without increasing overall system complexity uniformly across all image regions.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image based on their complexity. Simple regions receive lighter processing while complex regions receive more intensive processing, optimizing the balance between feature quality and computational resources without uniformly increasing system complexity.
2Measurement precision
If deeper processing networks are used to improve feature extraction quality, then image identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent dynamically adjusts the processing depth and network configuration based on the actual content and complexity of each image or image region. This dynamic adaptation allows the system to achieve high identification accuracy for complex images while maintaining fast processing for simpler images, resolving the time-accuracy tradeoff.
Solution Approach 2:
The patent applies full-depth processing only when necessary (for complex regions or images) rather than uniformly applying maximum processing to all inputs. This partial action approach maintains high accuracy where needed while reducing processing time for cases that don't require exhaustive analysis.
Data Source
AI summary
A global feature map processing method, an image identification method, and related apparatuses are provided. The method includes: obtaining a global feature map of a to-be-identified image; extracting, using a target channel attention model, low-order image information and high-order image information of the global feature map to perform a deep learning so as to obtain a low-order channel attention vector corresponding to the low-order image information and a high-order channel attention vector corresponding to the high-order image information; and obtaining an expected feature map of the to-be-identified image by performing an attention vector fusion weighted processing on the global feature map based on the low-order channel attention vector and the high-order channel attention vector. In this manner, a channel attention mechanism is introduced during the visual identification to fuse the low-order image information and high-order image information of the to-be-identified image for image feature extraction.