Global Aggregation Then Local Distribution for Scene Parsing.

Area of Science:

• Computer Vision
• Deep Learning
• Image Segmentation

Background:

• Convolutional Neural Networks (CNNs) struggle with long-range contextual relationships crucial for pixel-wise prediction tasks like semantic segmentation.
• Existing global aggregation methods can oversmooth fine details, introducing noise in critical regions such as object boundaries and small objects.

Purpose of the Study:

• To develop a method that accurately models long-range dependencies in semantic segmentation without sacrificing local detail.
• To propose a novel local distribution module that adaptively integrates global and local context for improved pixel-wise predictions.

Main Methods:

• Designed a local distribution module to model pixel-wise affinity between global and local relationships.
• Integrated this module with existing global aggregation techniques to create the GALD networks.
• Modularized the approach for easy integration into existing semantic segmentation architectures.

Main Results:

• The proposed GALD networks demonstrate state-of-the-art performance on major semantic segmentation benchmarks.
• Achieved superior results on datasets including Cityscapes, ADE20K, Pascal Context, Camvid, and COCO-stuff.
• The approach is versatile and can be easily plugged into existing semantic segmentation networks.

Conclusions:

• The GALD networks effectively address the limitations of CNNs in modeling long-range context for semantic segmentation.
• Integrating local context distribution enhances the accuracy of global information aggregation, preserving fine details.
• The proposed method offers a simple yet powerful solution for advancing semantic segmentation research.