Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators

TL;DR

This paper introduces Neural TMDlayer, a novel SDE-inspired module for computer vision that enhances existing models with minimal modifications, improving efficiency and performance across various tasks.

Contribution

It proposes a new SDE-based layer that models feature flow, offering a simple, efficient plug-in for existing architectures with demonstrated benefits in vision tasks.

Findings

Improved performance in few-shot learning tasks.

Enhanced efficiency in point cloud transformer models.

Better segmentation results with minimal architectural changes.

Abstract

We study how stochastic differential equation (SDE) based ideas can inspire new modifications to existing algorithms for a set of problems in computer vision. Loosely speaking, our formulation is related to both explicit and implicit strategies for data augmentation and group equivariance, but is derived from new results in the SDE literature on estimating infinitesimal generators of a class of stochastic processes. If and when there is nominal agreement between the needs of an application/task and the inherent properties and behavior of the types of processes that we can efficiently handle, we obtain a very simple and efficient plug-in layer that can be incorporated within any existing network architecture, with minimal modification and only a few additional parameters. We show promising experiments on a number of vision tasks including few shot learning, point cloud transformers and deep variational segmentation obtaining efficiency or performance improvements.