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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
Vector or Cross Product01:17

Vector or Cross Product

Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...
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Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

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  3. Da-cal: Towards Cross-domain Calibration In Semantic Segmentation.
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  3. Da-cal: Towards Cross-domain Calibration In Semantic Segmentation.

Related Experiment Video

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

DA-Cal: Towards Cross-Domain Calibration in Semantic Segmentation.

Wangkai Li, Rui Sun, Zhaoyang Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Existing unsupervised domain adaptation methods fail to ensure network calibration, risking safety. Our DA-Cal framework optimizes soft pseudo-labels for better calibration and improved semantic segmentation performance without extra inference cost.

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    Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

    Published on: February 23, 2017

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Unsupervised Domain Adaptation (UDA) methods improve semantic segmentation but often neglect network calibration.
    • Poor network calibration leads to misaligned confidence and accuracy, posing risks in safety-critical applications.
    • Soft pseudo-labels can degrade performance in cross-domain scenarios due to calibration issues.

    Purpose of the Study:

    • To propose DA-Cal, a novel cross-domain calibration framework for semantic segmentation.
    • To address the misalignment between prediction confidence and accuracy in UDA.
    • To enhance the reliability of semantic segmentation in safety-critical applications.

    Main Methods:

    • DA-Cal transforms target domain calibration into soft pseudo-label optimization.
  • A Meta Temperature Network generates pixel-level calibration parameters.
  • Bi-level optimization establishes the soft pseudo-label to UDA supervision relationship, using domain-mixing strategies.

    • Main Results:

    • DA-Cal significantly improves target domain calibration in semantic segmentation.
    • The framework seamlessly integrates with existing self-training UDA methods.
    • Performance gains are achieved without introducing additional inference overhead.

    Conclusions:

    • DA-Cal effectively enhances network calibration quality in unsupervised domain adaptation.
    • The proposed framework offers a robust solution for reliable semantic segmentation.
    • DA-Cal demonstrates broad applicability across various UDA segmentation benchmarks.