Densely connected multidilated convolutional networks for dense prediction tasks

11/21/2020
by   Naoya Takahashi, et al.
8

Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)-based approaches interchange representations in different resolutions only a few times. In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net). D3Net involves a novel multidilated convolution that has different dilation factors in a single layer to model different resolutions simultaneously. By combining the multidilated convolution with the DenseNet architecture, D3Net incorporates multiresolution learning with an exponentially growing receptive field in almost all layers, while avoiding the aliasing problem that occurs when we naively incorporate the dilated convolution in DenseNet. Experiments on the image semantic segmentation task using Cityscapes and the audio source separation task using MUSDB18 show that the proposed method has superior performance over state-of-the-art methods.

READ FULL TEXT

page 5

page 7

research
10/05/2020

D3Net: Densely connected multidilated DenseNet for music source separation

Music source separation involves a large input field to model a long-ter...
research
02/13/2023

RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget

Learning High-Resolution representations is essential for semantic segme...
research
10/31/2022

Hybrid CNN -Interpreter: Interpret local and global contexts for CNN-based Models

Convolutional neural network (CNN) models have seen advanced improvement...
research
05/24/2016

Dense CNN Learning with Equivalent Mappings

Large receptive field and dense prediction are both important for achiev...
research
04/30/2018

On the iterative refinement of densely connected representation levels for semantic segmentation

State-of-the-art semantic segmentation approaches increase the receptive...
research
11/20/2022

Real-time Local Feature with Global Visual Information Enhancement

Local feature provides compact and invariant image representation for va...
research
10/03/2022

Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features

Deep learning techniques have become prominent in modern fault diagnosis...

Please sign up or login with your details

Forgot password? Click here to reset