Search research articles

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Experimental Evidence of Néel-Order-Driven Magneto-optical Kerr Effect in an Altermagnetic Insulator.

Physical review letters·2026

Same author

Infrared imaging of magnetic octupole domains in non-collinear antiferromagnets.

National science review·2024

Same author

Boosting semi-supervised learning with Contrastive Complementary Labeling.

Neural networks : the official journal of the International Neural Network Society·2023

Same author

Improving fine-tuning of self-supervised models with Contrastive Initialization.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

[Overproduction of catalase by oxidative stress on Bacillus subtilis WSHDZ-01].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2009

Same author

[Construction of a RU486 inducible recombinant adenoviral vector carrying murine interleukin-12 gene and experimental treatment of colonic carcinoma].

Zhonghua yi xue za zhi·2009

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: Jun 17, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Enhanced Long-Tailed Recognition With Contrastive CutMix Augmentation.

Haolin Pan, Yong Guo, Mianjie Yu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 15, 2024

Summary

Contrastive CutMix (ConCutMix) improves deep learning for imbalanced datasets by using semantic information to create better labels for augmented data, boosting tail class recognition.

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

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jun 17, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

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

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Real-world datasets often exhibit long-tailed distributions, with few dominant classes and numerous rare classes.
Deep learning models struggle with generalization on tail classes due to data imbalance, leading to poor performance.

Purpose of the Study:

To address the limitations of traditional CutMix in handling imbalanced data by incorporating semantic information.
To propose Contrastive CutMix (ConCutMix) for improved long-tailed recognition.

Main Methods:

Contrastive learning is employed to derive semantic similarities between image samples.
These semantic similarities are used to refine the area-based labels generated by CutMix, creating semantically consistent labels.
The proposed ConCutMix method augments data for tail classes to enhance model generalization.

Main Results:

ConCutMix significantly enhances accuracy on tail classes and overall performance in long-tailed recognition tasks.
Experiments on ImageNet-LT using ResNeXt-50 demonstrated a 3.0% overall accuracy improvement, with a 3.3% gain on tail classes.
The effectiveness of ConCutMix was validated across different benchmarks and model architectures.

Conclusions:

Contrastive CutMix offers a robust solution for improving deep learning performance on imbalanced datasets.
The method's ability to generate semantically consistent labels is key to its success in long-tailed recognition.
ConCutMix provides a valuable technique for enhancing model generalization in real-world scenarios with data imbalance.