Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Aggregates Classification01:29

Aggregates Classification

345
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...
345
Classification of Systems-II01:31

Classification of Systems-II

177
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,
177
Classification of Signals01:30

Classification of Signals

532
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...
532
Classification of Systems-I01:26

Classification of Systems-I

215
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:
215
Methods of Classification and Identification01:28

Methods of Classification and Identification

37
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
37

You might also read

Related Articles

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

Sort by
Same author

Soil Carbon Stocks and Greenhouse Gas Mitigation of Agriculture in the Brazilian Cerrado-A Review.

Plants (Basel, Switzerland)·2023
See all related articles

Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

570

A Multi-Layer Feature Fusion Method for Few-Shot Image Classification.

Jacó C Gomes1, Lurdineide de A B Borges2, Díbio L Borges3

  • 1Department of Mechanical Engineering, University of Brasília, Brasília 70910-900, DF, Brazil.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

This study introduces a novel multi-layer feature fusion (FMLF) method to enhance few-shot image classification models. The FMLF approach improves accuracy and reduces parameters for recognizing visual categories with limited data.

Keywords:
few-shot learningmaize crop insect classificationmetric learningmulti-layer feature fusionmulti-scale features

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Related Experiment Videos

Last Updated: Jul 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

570
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Few-shot learning in image classification requires recognizing categories from minimal data.
  • Model feature expressiveness is critical for few-shot learning performance.
  • Developing simple yet effective few-shot deep learning architectures remains a challenge.

Purpose of the Study:

  • To propose an improved few-shot model using a multi-layer feature fusion (FMLF) method.
  • To enhance feature extraction and fusion mechanisms within Convolutional Neural Network (CNN) backbones.
  • To introduce a novel dataset for maize crop insect classification.

Main Methods:

  • Implemented a multi-layer feature fusion (FMLF) approach for enhanced feature extraction.
  • Integrated extended fusion mechanisms into CNN backbones.
  • Utilized an effective metric for computing divergence in few-shot classification.
  • Evaluated the model on a maize crop insect classification task.

Main Results:

  • The FMLF method demonstrated higher accuracy with fewer parameters compared to traditional backbones.
  • Achieved accuracy improvements of up to 3.62% in one-shot and 2.82% in five-shot tasks.
  • Outperformed standard backbones like ResNet50, VGG16, and MobileNetv2 in terms of accuracy and parameter efficiency.

Conclusions:

  • The proposed FMLF method offers a competitive and efficient solution for few-shot image classification.
  • This approach effectively addresses the challenge of feature expressiveness with limited data.
  • The novel maize dataset and FMLF model contribute to advancing agricultural image analysis.