Semantic Image Segmentation Training With Iterative Region Erasing
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Solution Overview
Problem
Existing semantic image segmentation methods using weakly-supervised training face challenges in accurately segmenting non-rigid objects due to the network's focus on most discriminative regions, leading to incomplete object classification.
Innovation Solution
A dual neural network approach where a first classifier network identifies discriminative regions, generates modified images by attenuating these regions, and a second network classifies these modified images, using different loss functions to iteratively improve the first network's performance, promoting broader object coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier neural network is trained to identify the most discriminative object regions, then classification accuracy is improved, but segmentation coverage deteriorates because the network focuses only on the most discriminative regions and misses significant parts of the object
Solution Approach 1:
The training process is segmented into multiple iterations where different regions are progressively identified. In each iteration, the network identifies discriminative regions, these regions are then masked out, and the network trains again to identify remaining object regions. This segmentation of the training process enables the network to learn both highly discriminative regions and less discriminative but still important regions, thereby improving segmentation coverage while maintaining classification accuracy.
Solution Approach 2:
Before final segmentation, the network performs preliminary classification to identify the most discriminative regions. These regions are then temporarily masked or down-weighted in subsequent training iterations, allowing the network to preliminarily learn what the obvious discriminative features are, and then progressively learn to identify additional regions that are less discriminative but still part of the object, resulting in more complete segmentation coverage.
2Loss of time
If weakly-supervised training with image-level labels is used, then training time and cost are reduced, but segmentation performance deteriorates due to the discriminative localization problem
Solution Approach 1:
The network employs feedback mechanisms where the identified discriminative regions from one iteration are used to modify the training data for the next iteration. Specifically, identified regions are masked or down-weighted, and the network receives feedback through loss functions that encourage it to identify additional object regions in subsequent iterations. This iterative feedback loop enables the network to progressively improve segmentation performance while maintaining the efficiency of weakly-supervised training with image-level labels.
3Productivity
If the network focuses on the most discriminative regions for classification, then classification efficiency is improved, but segmentation completeness deteriorates because significant parts of non-rigid objects are missed
Solution Approach 1:
The network's focus dynamically shifts across different iterations. In early iterations, the network efficiently identifies the most discriminative regions, maintaining high classification efficiency. As iterations progress, the discriminative regions are masked out, dynamically redirecting the network's attention to identify additional object regions. This dynamic adjustment of focus across iterations enables the network to maintain classification efficiency while progressively improving segmentation completeness for non-rigid objects.
Data Source
AI summary
The invention relates to a method of training a model for use in semantic image segmentation. The model includes a first classifier neural network and a second classifier neural network. The method includes training the first network by inputting a first training image containing a target object to the first network, using the first network to identify and erase pixels of the first training image that are discriminative for the target object, inputting the first training image to the second network, using the second network to determine a likelihood of the first modified training image containing the target object and updating weights of the first network using a first loss function that is a monotonically-increasing function of the determined likelihood.