Image Classification Training with Offset Networks for Semantic Segmentation
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Solution Overview
Problem
The high cost and low efficiency of manual pixel-level annotation in training semantic image segmentation networks, particularly for images with resolutions like 1024×2048, result in excessive manual annotation costs and reduced training efficiency.
Innovation Solution
A method for training an image classification model using an offset network and image classification network, where model parameters are adjusted through a classification loss function to reduce the need for manual pixel-level annotation, enabling the training of semantic image segmentation networks with image-level annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel-level annotation is used for training semantic image segmentation networks, then the model can achieve accurate segmentation performance, but the annotation cost becomes excessively high and training efficiency decreases
Solution Approach 1:
The method performs preliminary full-image classification to generate initial segmentation regions before detailed pixel-level processing. By pre-identifying content regions through image-level classification, the system reduces the scope of subsequent pixel-level annotation work, thereby decreasing annotation time while preserving segmentation accuracy.
Solution Approach 2:
The annotation process is segmented into two stages: first, image-level classification to identify content regions; second, pixel-level processing only within those identified regions. This segmentation of the annotation task reduces overall annotation time while maintaining accuracy in the regions that matter most.
2Loss of information
If manual pixel-level annotation is performed on high-resolution images (e.g., 1024×2048), then complete segmentation information can be obtained, but the annotation cost becomes excessively high
Solution Approach 1:
The system applies different processing qualities to different regions: image-level classification is applied to the entire high-resolution image to identify content regions, while pixel-level processing is applied locally only to those identified regions. This local quality approach ensures complete segmentation information where needed while reducing overall annotation cost.
Solution Approach 2:
The method extracts and processes only the relevant content regions identified by the classification network, rather than processing the entire high-resolution image uniformly. By taking out and focusing on only the necessary regions, the system maintains information completeness while reducing annotation cost.
3Difficulty of detecting and measuring
If full-image classification is used to locate image content regions, then the regions can be identified for segmentation, but the process still requires pixel-level annotation which reduces training efficiency
Solution Approach 1:
The method merges the content region location function with the segmentation training process. The classification network's content region detection capability is combined with the segmentation network training, allowing the system to both locate regions accurately and train the segmentation model simultaneously, thereby improving training efficiency without sacrificing location accuracy.
Solution Approach 2:
The classification network serves multiple functions: it locates image content regions, provides supervision signals for segmentation training, and identifies areas requiring pixel-level processing. This multi-functionality improves training efficiency by eliminating the need for separate processing steps while maintaining accurate content region detection.
Data Source
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AI summary
Disclosed is an image classification model training method, comprising: obtaining an image to be trained; when a first model parameter of an offset network to be trained is fixed, obtaining first prediction category marking information of said image by means of an image classification network to be trained; determining a second model parameter using a classification loss function according to image content category information and the first prediction category marking information; when a second model parameter of said image classification network is fixed, obtaining second prediction category marking information of said image by means of said offset network; determining a third model parameter using the classification loss function according to the image content category information and the second prediction category marking information; and obtaining an image semantic segmentation network model according to the second model parameter and the third model parameter. The present application implements pixel-level marking and model training by means of AI, and manual marking is not needed, so that the manual marking cost is lowered, and the model training efficiency is improved.