PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

Two-branch network architecture has shown its efficiency and effectiveness in real-time semantic segmentation tasks. Our family of PIDNets achieve the best trade-off between inference speed and accuracy and their accuracy surpasses all the existing models with similar inference speed on the Cityscapes and CamVid datasets.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 However, direct fusion of high-resolution details and low-frequency context has the drawback of detailed features being easily overwhelmed by surrounding contextual information.
• 2 This overshoot phenomenon limits the improvement of the segmentation accuracy of existing two-branch models.
• 3 In this paper, we make a connection between Convolutional Neural Networks (CNN) and Proportional-Integral-Derivative (PID) controllers and reveal that a two-branch network is equivalent to a Proportional-Integral (PI) controller, which inherently suffers from similar overshoot issues.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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