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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Transformers in Distribution System01:27

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Transformers with Off-Nominal Turns Ratios

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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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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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Instance Sequence Queries for Video Instance Segmentation with Transformers.

Zhujun Xu1, Damien Vivet1

  • 1Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), University of Toulouse, 31400 Toulouse, France.

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|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel transformer-based method for video instance segmentation (VIS). It eliminates complex post-processing by using instance sequence queries for frame-to-frame segmentation, achieving competitive results.

Keywords:
querytransformervideo instance segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current video instance segmentation (VIS) methods often rely on complex post-processing or 3D spatial-temporal modeling, which has limitations.
  • These existing approaches face challenges with computational efficiency and memory constraints.

Purpose of the Study:

  • To propose a novel frame-to-frame video instance segmentation (VIS) method using transformers.
  • To overcome the limitations of existing VIS techniques by avoiding complex data association.

Main Methods:

  • A transformer-based architecture utilizing instance sequence queries (ISQs) to drive frame-level segmentation.
  • An extended bipartite matching loss for training, enabling ISQs to adapt during inference.
  • Preserving instance consistency through ordered query slots and network outputs.

Main Results:

  • The proposed method achieves competitive performance on the Youtube-VIS dataset.
  • Achieved 34.4% mAP at 33.5 FPS (ResNet-50) and 35.5% mAP at 26.6 FPS (ResNet-101).
  • Demonstrates effective instance consistency without complex post-processing.

Conclusions:

  • The frame-to-frame transformer approach offers an efficient and effective solution for video instance segmentation.
  • This method simplifies the VIS pipeline by removing the need for intricate data association steps.
  • The results highlight the potential of transformer-based models for real-time video analysis tasks.