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

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

You might also read

Related Articles

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

Sort by

Same author

Correction: A new criterion for defining tunnel portal failure using the strength reduction method.

PloS one·2026

Same author

ANXA2 promotes NLRP3 inflammasome activation and neuronal pyroptosis after intracerebral hemorrhage.

Frontiers in immunology·2026

Same author

Towards controllable video synthesis of routine and rare OR events.

International journal of computer assisted radiology and surgery·2026

Same author

Prevalence of Cardiometabolic Abnormalities Among Male Licensed Pilots and Community Health Examination Participants in China: A Cross-Sectional Comparison.

International journal of general medicine·2026

Same author

TwinOR: photorealistic digital twins of dynamic operating rooms for embodied AI research.

International journal of computer assisted radiology and surgery·2026

Same author

A new criterion for defining tunnel portal failure using the strength reduction method.

PloS one·2026

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multimodal prototypical network for interpretable sentiment classification.

Chenguang Song¹, Ke Chao², Bingjing Jia²

¹Anhui Science and Technology University, Bengbu, 233000, China. songcg@ahstu.edu.cn.

Scientific Reports

|October 27, 2025

Summary

This summary is machine-generated.

This study introduces MultiModal Prototypical Networks (MMPNet) for multimodal sentiment analysis. MMPNet enhances model interpretability by identifying contributions of temporal segments and modality features, improving accuracy on video datasets.

Keywords:

Interpretability Multimodal prototypical networks Multimodal sentiment analysis

Related Experiment Videos

Last Updated: Jan 6, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Sentiment analysis increasingly uses multimodal video data (visual, acoustic, textual).
Limited understanding exists on temporal segment contributions to model decisions in multimodal sentiment analysis.
Existing interpretable methods struggle with multimodal interactions and temporal dependencies in video.

Purpose of the Study:

To extend prototype-based interpretability to multimodal sentiment classification.
To develop a method that identifies temporal segment contributions and modality-level feature importance.
To improve the explainability of multimodal sentiment analysis models.

Main Methods:

Proposed MultiModal Prototypical Networks (MMPNet) for multimodal sentiment classification.
Extended prototype-based interpretability to handle multimodal video data.
Developed techniques to identify time-level feature contributions and modality-level importance.

Main Results:

MMPNet achieved superior performance, outperforming existing methods by 2.9% on CMU-MOSI and 1.6% on CMU-MOSEI.
Demonstrated improved accuracy in multimodal sentiment classification tasks.
Provided enhanced interpretability for model predictions.

Conclusions:

MMPNet offers a novel approach to interpretable multimodal sentiment analysis.
The method effectively explains predictions by analyzing temporal and modality features.
MMPNet sets a new benchmark for accuracy and interpretability in video-based sentiment analysis.