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Learning to Exploit the Prior Network Knowledge for Weakly Supervised Semantic Segmentation
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DOI：10.1109/TIP.2019.2901393.png)
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Abstract
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Training a convolutional neural network for semantic segmentation typically requires collecting a large amount of accurate pixel-level annotations and is a hard and expensive task. In contrast, simple image tags are easier to gather. In this paper, we introduce a novel weakly supervised semantic segmentation model which is able to learn from image labels and just image labels. Our model uses the prior knowledge of a network trained for image recognition, employing these image annotations as an attention mechanism to identify semantic regions in the images. We then present a methodology that builds accurate class-specific segmentation masks from these regions, where neither external objectness nor saliency algorithms are required. We describe how to incorporate this mask generation strategy into a fully end-to-end trainable process, where the network jointly learns to classify and segment images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these generated class-specific masks in conjunction with our novel end-to-end learning process outperforms several recent weakly supervised semantic segmentation methods that use image tags only, and even some models that leverage additional supervision or training data.
Keywords:
Semantic segmentation
weakly supervised
deep learning
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