PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond

TL;DR

PPLNs are a novel neural network architecture inspired by biological neural principles, designed for efficient event-based temporal vision tasks, achieving state-of-the-art results in various vision applications.

Contribution

Introduction of Parametric Piecewise Linear Networks (PPLNs), a new neural model that leverages learnable piecewise linear functions for event-based and image-based vision tasks.

Findings

Achieved state-of-the-art performance in event-based steering prediction.

Demonstrated superior results in human pose estimation.

Effective in motion deblurring applications.

Abstract

We present Parametric Piecewise Linear Networks (PPLNs) for temporal vision inference. Motivated by the neuromorphic principles that regulate biological neural behaviors, PPLNs are ideal for processing data captured by event cameras, which are built to simulate neural activities in the human retina. We discuss how to represent the membrane potential of an artificial neuron by a parametric piecewise linear function with learnable coefficients. This design echoes the idea of building deep models from learnable parametric functions recently popularized by Kolmogorov-Arnold Networks (KANs). Experiments demonstrate the state-of-the-art performance of PPLNs in event-based and image-based vision applications, including steering prediction, human pose estimation, and motion deblurring. The source code of our implementation is available at https://github.com/chensong1995/PPLN.