Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.5K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

580
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
580
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
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...
140
Variability: Analysis01:11

Variability: Analysis

189
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
189
State Space Representation01:27

State Space Representation

283
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
283
Signal Flow Graphs01:18

Signal Flow Graphs

313
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
313

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Semantic-Decoupled and Knowledge-Shared Probabilistic Mapping Network for Multi-Grained Cross-Modal Retrieval.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Adaptive 3D Convolution for Remote Sensing Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Tensor Wheel Decomposition: Theory and Application to Tensor Completion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Evolutionary Dynamics of Variable Games in Structured Populations.

IEEE transactions on cybernetics·2026
Same author

Computing-in-memory architecture for Kolmogorov-Arnold networks based on tunable Gaussian-like memory cells.

Nature communications·2026
Same author

A General Image Fusion Approach Exploiting Gradient Transfer Learning and Fusion Rule Unfolding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570

ビデオ要約のためのスピーキング変数グラフ表現推論

Wenrui Li, Wei Han, Liang-Jian Deng

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 1, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,効率的なビデオ要約のためのスピーキングバリエーショングラフ (SpiVG) ネットワークを導入します. SpiVGは,スパイキングニューラルネットワーク (SNN) とダイナミックグラフ推論を使用して,情報の密度を高め,複雑さを軽減します.

    さらに関連する動画

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    681

    関連する実験動画

    Last Updated: Sep 9, 2025

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    570
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    681

    科学分野:

    • 人工知能
    • コンピュータ・ビジョン
    • 機械学習

    背景:

    • 効率的なビデオの要約は,短いビデオコンテンツの拡散のために非常に重要です.
    • 既存の方法は,時間的な依存性,意味論的な一貫性を捉える上で困難に直面し,特徴融合の際にノイズに敏感です.

    研究 の 目的:

    • スパイキング・バリエーション・グラフ (SpiVG) ネットワークを提案し,ビデオの要約を改善する.
    • 情報の密度を高め,ビデオの要約の計算の複雑さを軽減します.

    主な方法:

    • スパイキングニューラルネットワーク (SNN) を使用したキーフレーム抽出器を開発した.
    • ダイナミック・アグレゲーション・グラフ・リザネージャー (Dynamic Aggregation Graph Reasoner) を導入し,ビデオフレームにわたって細かい粒度で適応可能な推論を行いました.
    • マルチチャネル機能融合ノイズと不確実性を処理するために,エビデンス下限最適化 (ELBO) によるバリエーション推論再構築モジュールを実装しました.

    主要な成果:

    • SpiVGネットワークは,複数のベンチマークデータセット (SumMe,TVSum,VideoXum,QFVS) で既存の方法と比較して優れたパフォーマンスを示しました.
    • 提案された方法は,時間依存性,意味的一貫性,およびノイズ削減の課題を効果的に解決します.

    結論:

    • SpiVGネットワークは,効率的で正確なビデオの要約を大幅に改善します.
    • このアプローチは,SNNとグラフ推論を効果的に活用して,強力なビデオコンテンツ分析を行います.