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Related Concept Videos

Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

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When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
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One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
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Transformers with Off-Nominal Turns Ratios01:25

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Deformation of Member under Multiple Loadings01:11

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When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
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Types Of Transformers01:16

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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DeforT: Deformable transformer for visual tracking.

Kai Yang1, Qun Li2, Chunwei Tian3

  • 1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China; Hubei Luojia Laboratory, Wuhan 430200, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deformable transformer for visual object tracking, improving feature correlation and localization accuracy. The novel approach enhances tracking robustness and performance on benchmark datasets.

Keywords:
Classification networkDeformable transformerRegression networkVisual tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional visual trackers often rely on classification and bounding box regression.
  • Linear correlation methods in trackers can lose semantic features and lead to local optima.
  • Existing trackers suffer from performance degradation due to unreliable classification scores and Intersection over Union (IoU) loss for regression training.

Purpose of the Study:

  • To develop a deformable transformer model for more effective visual object tracking.
  • To address the limitations of semantic feature loss and local optima in current tracking methods.
  • To improve both classification accuracy and localization precision in visual tracking.

Main Methods:

  • Utilized a deformable transformer to compute correlation features between training and search sets.
  • Introduced a quality-aware focal loss (QAFL) for classification network training to resolve prediction inconsistencies.
  • Employed an alpha-Generalized Intersection over Union (α-GIoU) loss for regression network training to enhance localization accuracy.
  • Integrated online learning scores with a transformer-assisted framework and classification scores for robust candidate object location prediction.

Main Results:

  • The deformable transformer model effectively computes correlation features, overcoming limitations of linear methods.
  • The quality-aware focal loss (QAFL) improved the consistency between classification and localization quality predictions.
  • The alpha-Generalized Intersection over Union (α-GIoU) loss significantly boosted localization accuracy.
  • The proposed method achieved a 71.7% success score on OTB-2015 and a 67.3% AUC score on NFS30, demonstrating superior performance.

Conclusions:

  • The deformable transformer model offers a significant advancement in visual object tracking.
  • The novel loss functions (QAFL and α-GIoU) effectively address key challenges in tracking accuracy and robustness.
  • The proposed method demonstrates state-of-the-art performance on multiple challenging benchmark datasets.