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

Related Concept Videos

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

Parseval's Theorem

Parseval's Theorem

Parseval's theorem is a fundamental concept in signal processing and harmonic analysis. It asserts that for a periodic function, the average power of the signal over one period equals the sum of the squared magnitudes of all its complex Fourier coefficients. This theorem, named after Marc-Antoine Parseval, provides a powerful tool for analyzing the energy distribution in signals.
Interestingly, Parseval's theorem also holds for the trigonometric form of the Fourier series, which...

Deductive Reasoning

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Weak Base Solutions

Weak Base Solutions

Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

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

Sort by

Same author

Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Survey on the current status of rabies post-exposure prophylaxis clinics construction and the surveillance of rabies-exposed individuals in China, 2022.

PLoS neglected tropical diseases·2026

Same author

miR-140-5p Inhibition alleviates LPS-induced cholangitis in rats and is associated with modulation of the NF-κB signaling pathway.

Scientific reports·2026

Same author

Saturation of space weathering in shaping lunar regolith particle morphology.

Nature communications·2026

Same author

Learning Efficient Meshflow and Optical Flow From Event Cameras.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Epidemiological Characteristics and Influencing Factors of Bacillary Dysentery Outbreaks - China, 2008-2024.

China CDC weekly·2025

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Robust Scene Parsing by Mining Supportive Knowledge From Dataset.

Ao Luo, Fan Yang, Xin Li

IEEE Transactions on Neural Networks and Learning Systems

|September 14, 2021

Summary

This summary is machine-generated.

This study introduces a new method for scene parsing, enhancing deep learning models by incorporating dataset-wide knowledge. This knowledge augmentation improves pixel-level image understanding and achieves state-of-the-art results.

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Oct 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Scene parsing, also known as semantic segmentation, involves assigning a category label to every pixel in an image.
Current state-of-the-art methods use deep neural networks but primarily learn representations from individual images, neglecting broader dataset knowledge.
This limitation hinders the ability of models to generalize and robustly parse complex scenes.

Purpose of the Study:

To enhance scene parsing by leveraging generic knowledge from the entire dataset, not just individual images.
To develop a novel approach that integrates dataset-level supportive knowledge with image-specific content for improved representation learning.
To introduce a Knowledge Augmented Neural Network (KANN) that boosts representational power for semantic segmentation.

Main Methods:

Proposed a novel Supportive Knowledge Mining Module (SKMM) to extract general visual concepts and their relationships from the dataset.
Introduced a Knowledge Augmentation Operator (KAO) for seamless integration into existing scene parsing networks.
Developed the Knowledge Augmented Neural Network (KANN) by combining image-specific content with mined dataset-level knowledge.

Main Results:

The KANN model demonstrated superior performance across three challenging datasets: Cityscapes, Pascal-Context, and ADE20K.
Experimental results confirmed that KANN significantly outperforms existing state-of-the-art methods in scene parsing accuracy.
The integration of supportive knowledge led to enhanced representational power and better scene understanding.

Conclusions:

Incorporating dataset-level supportive knowledge is a crucial step towards more robust and accurate scene parsing.
The proposed SKMM and KAO provide an effective and pluggable solution for knowledge augmentation in deep learning models.
KANN represents a significant advancement in semantic segmentation, setting a new benchmark for performance.