Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Semantic segmentation models typically require extensive pixel-level annotations, which are costly and time-consuming to acquire.
• Image-level tags are significantly easier and cheaper to collect, offering a potential alternative for training segmentation models.

Purpose of the Study:

• To develop a novel weakly-supervised semantic segmentation model that learns solely from image-level labels.
• To introduce a methodology for generating accurate class-specific segmentation masks without external algorithms.
• To integrate this mask generation into an end-to-end trainable framework for joint image classification and segmentation.

Main Methods:

• Leveraged prior knowledge from a pre-trained image recognition network as an attention mechanism to identify semantic regions.
• Developed a novel strategy to generate class-specific segmentation masks from identified regions.
• Implemented an end-to-end training process for joint classification and segmentation using generated masks.

Main Results:

• The proposed model successfully learns semantic segmentation from image tags alone.
• Generated segmentation masks were accurate and did not require external objectness or saliency algorithms.
• The end-to-end learning process with generated masks outperformed recent weakly-supervised methods on the PASCAL VOC 2012 dataset.

Conclusions:

• Weakly-supervised semantic segmentation can be effectively achieved using only image-level tags.
• The proposed attention-based mask generation and end-to-end training offer a more efficient approach to semantic segmentation.
• This method demonstrates superior performance compared to existing weakly-supervised techniques, even those with additional supervision.