Presentation Title

Deep Learning Based Perspective Transformation Layer

Presenter Information

Nishan KhatriFollow

Advisor Information

Xin Zhong

Location

MBSC Gallery Room 308 - G

Presentation Type

Oral Presentation

Start Date

4-3-2022 12:30 PM

End Date

4-3-2022 1:45 PM

Abstract

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on affine transformations that cannot fully show viewpoint changes. Furthermore, current solutions often apply a neural network module to learn a single transformation matrix, which ignores the possibility for various viewpoints and creates extra to-be-trained module parameters. We propose a layer (PT layer) to learn the perspective transformations that not only model the geometries in affine transformation but also reflect the viewpoint changes. In addition, being able to be directly trained with gradient descent like traditional layers such as convolutional layers, a single proposed PT layer can learn an adjustable number of multiple viewpoints without training extra module parameters.

This document is currently not available here.

COinS
 
Mar 4th, 12:30 PM Mar 4th, 1:45 PM

Deep Learning Based Perspective Transformation Layer

MBSC Gallery Room 308 - G

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on affine transformations that cannot fully show viewpoint changes. Furthermore, current solutions often apply a neural network module to learn a single transformation matrix, which ignores the possibility for various viewpoints and creates extra to-be-trained module parameters. We propose a layer (PT layer) to learn the perspective transformations that not only model the geometries in affine transformation but also reflect the viewpoint changes. In addition, being able to be directly trained with gradient descent like traditional layers such as convolutional layers, a single proposed PT layer can learn an adjustable number of multiple viewpoints without training extra module parameters.