TAGNet: Learning Configurable Context Pathways for Semantic Segmentation.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Semantic segmentation methods rely on pixel relationships for contextual information.
• Existing methods use fixed pathways, limiting context flexibility for individual pixels.

Purpose of the Study:

• Introduce Configurable Context Pathways (CCPs) for flexible contextual information augmentation.
• Develop TAGNet to adaptively configure regions for information flow between pixels.

Main Methods:

• Learned pathways using configurable regions to establish pixel-to-pixel information flows.
• TAGNet adaptively configures regions across the image based on remote pixel relationships.
• Information flow is refined through sequences of configurable regions.

Main Results:

• Extensive evaluation of Traveling, Adaptation, and Gathering (TAG) stages on public benchmarks.
• All stages demonstrated significant improvements in semantic segmentation accuracy.
• Achieved state-of-the-art results, outperforming existing methods.

Conclusions:

• CCPs offer a novel and effective approach to contextual information exchange in semantic segmentation.
• TAGNet's adaptive configuration and gradual information refinement enhance segmentation performance.
• The proposed method sets a new benchmark for semantic segmentation accuracy.