SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation

06/08/2021
by   Taehun Kim, et al.
0

Semantic segmentation networks adopt transfer learning from image classification networks which occurs a shortage of spatial context information. For this reason, we propose Spatial Context Memoization (SpaM), a bypassing branch for spatial context by retaining the input dimension and constantly communicating its spatial context and rich semantic information mutually with the backbone network. Multi-scale context information for semantic segmentation is crucial for dealing with diverse sizes and shapes of target objects in the given scene. Conventional multi-scale context scheme adopts multiple effective receptive fields by multiple dilation rates or pooling operations, but often suffer from misalignment problem with respect to the target pixel. To this end, we propose Meshgrid Atrous Convolution Consensus (MetroCon^2) which brings multi-scale scheme into fine-grained multi-scale object context using convolutions with meshgrid-like scattered dilation rates. SpaceMeshLab (ResNet-101 + SpaM + MetroCon^2) achieves 82.0 53.5

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 4

07/07/2019

ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning

Extracting multi-scale information is key to semantic segmentation. Howe...
09/05/2019

Semantic Correlation Promoted Shape-Variant Context for Segmentation

Context is essential for semantic segmentation. Due to the diverse shape...
01/11/2022

Pyramid Fusion Transformer for Semantic Segmentation

The recently proposed MaskFormer <cit.> gives a refreshed perspective on...
07/13/2019

Adaptive Context Encoding Module for Semantic Segmentation

The object sizes in images are diverse, therefore, capturing multiple sc...
03/23/2021

Dilated SpineNet for Semantic Segmentation

Scale-permuted networks have shown promising results on object bounding ...
05/22/2018

Autofocus Layer for Semantic Segmentation

We propose the autofocus convolutional layer for semantic segmentation w...
06/13/2020

Split-Merge Pooling

There are a variety of approaches to obtain a vast receptive field with ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.