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Transformers

Transformers

A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...

Types Of Transformers

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...

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Related Experiment Video

Updated: Sep 30, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

SST: Spatial and Semantic Transformers for Multi-Label Image Recognition.

Zhao-Min Chen, Quan Cui, Borui Zhao

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

|March 11, 2022

Summary

This summary is machine-generated.

This study introduces Spatial and Semantic Transformers (SST) to improve multi-label image recognition by capturing both spatial and semantic label correlations simultaneously. The new method significantly outperforms existing approaches on benchmark datasets.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Multi-label image recognition aims to assign multiple labels to images.
Existing methods often focus on either spatial or semantic label correlations, but not both.
Transformers have shown success in capturing long-range dependencies in data.

Purpose of the Study:

To propose a novel module, Spatial and Semantic Transformers (SST), for multi-label image recognition.
To simultaneously capture spatial and semantic correlations within images.
To demonstrate the complementary nature of spatial and semantic correlations.

Main Methods:

Developed a plug-and-play module named Spatial and Semantic Transformers (SST).
SST consists of two independent transformers: one for spatial correlations and one for semantic correlations.
Spatial Transformer models correlations between features at different spatial locations.
Semantic Transformer captures label co-occurrence without predefined rules.

Main Results:

The proposed SST module significantly outperforms state-of-the-art methods on four benchmark datasets.
Demonstrated that spatial and semantic correlations are complementary and beneficial when captured together.
Ablation studies and visualizations validated the effectiveness of the SST components.

Conclusions:

Simultaneously capturing spatial and semantic correlations is crucial for advancing multi-label image recognition.
The proposed Spatial and Semantic Transformers (SST) module offers an effective and generalizable approach.
This work highlights the potential of Transformer-based architectures for complex image recognition tasks.