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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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Updated: Jan 18, 2026

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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TATrack: Target-oriented adaptive vision transformer for UAV tracking.

Wenkang Zhang1, Tianyang Xu2, Fei Xie3

  • 1School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

We introduce TATrack, a novel adaptive vision transformer for Unmanned Aerial Vehicle (UAV) tracking. It balances high accuracy and computational efficiency by intelligently adapting its network layers for faster inference.

Keywords:
Adaptive vision transformerObject trackingTarget orientationUAV

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Unmanned Aerial Vehicle (UAV) tracking demands precise aerial target localization under strict computational limitations.
  • Existing UAV trackers often rely on lightweight Convolutional Neural Networks (CNNs) and appearance-based fusion, limiting comprehensive target perception and the accuracy-efficiency balance.
  • The lack of robust target perception hinders the performance of current UAV tracking systems.

Purpose of the Study:

  • To develop a target-oriented adaptive vision transformer for Unmanned Aerial Vehicle (UAV) tracking.
  • To enhance the balance between tracking accuracy and computational efficiency in aerial tracking scenarios.
  • To improve target localization and temporal continuity in UAV tracking.

Main Methods:

  • Proposed TATrack, a novel efficient transformer model (TA-ViT) for joint feature modeling and interaction guided by the target's orientation.
  • Implemented an adaptive scoring suspension mechanism in TA-ViT to bypass redundant network layers, accelerating inference speed.
  • Utilized positional information as a spatial-temporal prompt to improve appearance-matching quality and strengthen visual perception with location priors.

Main Results:

  • Demonstrated an optimal balance between computational efficiency and tracking accuracy across five UAV tracking benchmarks.
  • Achieved improved target orientation and temporal continuity in predicted positions through enhanced visual perception.
  • The proposed method significantly outperforms existing approaches in UAV tracking performance.

Conclusions:

  • TATrack effectively addresses the limitations of current UAV trackers by offering superior accuracy and computational efficiency.
  • The adaptive transformer architecture and use of location priors represent a significant advancement in aerial tracking technology.
  • The developed method provides a promising solution for real-world UAV tracking applications.