Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 2, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation.

Bruno Artacho¹, Andreas Savakis¹

¹Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.

Sensors (Basel, Switzerland)

|December 11, 2019

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

On Denoising Diffusion Probabilistic Models for Synthetic Aperture Radar Despeckling.

Sensors (Basel, Switzerland)·2025

Same author

On the Importance of Attention and Augmentations for Hypothesis Transfer in Domain Adaptation and Generalization.

Sensors (Basel, Switzerland)·2023

Same author

Video Captioning Using Global-Local Representation.

IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society·2023

Same author

Full-BAPose: Bottom Up Framework for Full Body Pose Estimation.

Sensors (Basel, Switzerland)·2023

Same author

Perceptual optimization of language: Evidence from American Sign Language.

Cognition·2022

Same author

Benchmarking Domain Adaptation Methods on Aerial Datasets.

Sensors (Basel, Switzerland)·2021

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

We introduce a novel "Waterfall" architecture for semantic segmentation, boosting accuracy while reducing network parameters and memory usage. This efficient method eliminates the need for post-processing, saving training time and complexity.

Area of Science:

Computer Vision
Deep Learning
Image Analysis

Background:

Semantic segmentation is crucial for image understanding tasks.
Existing methods often require significant computational resources and complex post-processing.
There is a need for efficient architectures that maintain high accuracy.

Purpose of the Study:

To propose a new, efficient architecture for semantic segmentation.
To improve accuracy while reducing network parameters and memory footprint.
To eliminate the reliance on post-processing stages like Conditional Random Fields.

Main Methods:

Developed a novel "Waterfall" Atrous Spatial Pooling architecture.
Leveraged progressive filtering for efficiency and multiscale field-of-view.

Keywords:

atrous convolution computer vision semantic segmentation spatial pooling

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jan 2, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Integrated with a ResNet backbone for robust performance.

Main Results:

Achieved a considerable increase in accuracy for semantic segmentation.
Significantly decreased the number of network parameters and memory footprint.
Obtained state-of-the-art results on the Pascal VOC and Cityscapes datasets without post-processing.

Conclusions:

The Waterfall architecture offers a robust and efficient solution for semantic segmentation.
This approach reduces computational complexity and training time.
It represents a significant advancement in efficient deep learning for computer vision.