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Gated Path Selection Network for Semantic Segmentation.

Qichuan Geng, Hong Zhang, Xiaojuan Qi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 8, 2021

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This study introduces the Gated Path Selection Network (GPSNet) for semantic segmentation. GPSNet adaptively selects receptive fields to improve performance on challenging datasets like Cityscapes and ADE20K.

Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Semantic segmentation is complex, requiring handling of scale variations, deformations, and diverse viewpoints.
Existing methods often focus on sparse sampling, limiting context harvesting.

Purpose of the Study:

To introduce a novel network, Gated Path Selection Network (GPSNet), for adaptive receptive field selection in semantic segmentation.
To enhance dense semantic context aggregation for improved segmentation accuracy.

Main Methods:

Designed a two-dimensional SuperNet to incorporate features from growing receptive fields.
Developed a Comparative Feature Aggregation (CFA) module for dynamic aggregation of discriminative semantic context.
Enabled adaptive harvesting of free-form dense semantic context information.

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Main Results:

GPSNet demonstrates adaptive receptive fields and dense sampling locations that are data-dependent and flexible.
The approach effectively models various object contexts.
Consistently outperforms previous methods on Cityscapes and ADE20K datasets.

Conclusions:

GPSNet offers a flexible and effective solution for semantic segmentation challenges.
The adaptive nature of the network allows for superior context modeling.
The proposed method achieves state-of-the-art results without complex additions.