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An Efficient Sampling-Based Attention Network for Semantic Segmentation.

Xingjian He, Jing Liu, Weining Wang

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

|March 30, 2022

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This study introduces an efficient Sampling-based Attention Network for semantic segmentation. It reduces computational costs by using novel sampling methods while maintaining high performance in image analysis.

Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Self-attention mechanisms are crucial for modeling long-range dependencies in semantic segmentation.
However, standard self-attention incurs high computational complexity due to pair-wise computations.
Efficient attention mechanisms are needed to address these limitations.

Purpose of the Study:

To propose an efficient Sampling-based Attention Network for semantic segmentation.
To reduce the computational redundancy associated with traditional self-attention.
To achieve competitive or superior performance compared to state-of-the-art methods.

Main Methods:

Introduced a Stochastic Sampling-based Attention Module (SSAM) for global context capture.

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Developed a Deterministic Sampling-based Attention Module (DSAM) for local feature extraction.

Combined these modules to create an efficient attention network.

Main Results:

The proposed SSAM achieves comparable segmentation performance to self-attention with significantly reduced computational cost.
DSAM effectively captures detailed local information.
The overall method demonstrates strong performance across multiple benchmark datasets.

Conclusions:

The Sampling-based Attention Network offers an efficient and effective solution for semantic segmentation.
The novel SSAM and DSAM modules provide a balance between global context and local detail.
The method shows promise for real-world applications requiring high-performance semantic segmentation.