DeepAI
Log In Sign Up

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

10/05/2022
by   Chen Liang, et al.
26

Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature,class). For each class, GMMSeg builds Gaussian Mixture Models (GMMs) via Expectation-Maximization (EM), so as to capture class-conditional densities. Meanwhile, the deep dense representation is end-to-end trained in a discriminative manner, i.e., maximizing p(class|pixel feature). This endows GMMSeg with the strengths of both generative and discriminative models. With a variety of segmentation architectures and backbones, GMMSeg outperforms the discriminative counterparts on three closed-set datasets. More impressively, without any modification, GMMSeg even performs well on open-world datasets. We believe this work brings fundamental insights into the related fields.

READ FULL TEXT

page 2

page 8

page 9

page 18

page 19

page 20

page 21

07/19/2022

Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation

The semantic segmentation task aims at dense classification at the pixel...
09/29/2017

A Gaussian mixture model representation of endmember variability in hyperspectral unmixing

Hyperspectral unmixing while considering endmember variability is usuall...
06/11/2021

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation

In semantic segmentation, we aim to train a pixel-level classifier to as...
12/28/2016

Superpixel Segmentation Using Gaussian Mixture Model

Superpixel segmentation algorithms are to partition an image into percep...
04/25/2018

Learning a Discriminative Feature Network for Semantic Segmentation

Most existing methods of semantic segmentation still suffer from two asp...
06/06/2022

FuSS: Fusing Superpixels for Improved Segmentation Consistency

In this work, we propose two different approaches to improve the semanti...
08/23/2018

Discriminative out-of-distribution detection for semantic segmentation

This paper considers dense detection of out-of-distribution pixels. As a...