Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Downsampling01:20

Downsampling

158
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
158
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

327
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...
327
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

74
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.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
74
Aggregates Classification01:29

Aggregates Classification

326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Trimmed Mean01:10

Trimmed Mean

2.9K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
2.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dataset Pruning: Reducing Training Data by Examining SGD-Influence.

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

COMBINER: Composed Image Retrieval Guided by Attribute-Based Neighbor Relations.

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

UniEmo: Unifying Emotional Understanding and Generation With Learnable Expert Queries.

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

SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video.

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

A Natural Language Guided Approach for Blind Face Restoration: Methodology and Dataset.

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

FRM-PTQ: Feature relationship matching enhanced low-bit post-training quantization for large language models.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

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

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

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

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jul 5, 2025

Video-rate Scanning Confocal Microscopy and Microendoscopy
14:10

Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

27.9K

Query-Oriented Micro-Video Summarization.

Mengzhao Jia, Yinwei Wei, Xuemeng Song

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 18, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new model for query-oriented micro-video summarization, generating search queries from videos. The QMS model improves both summarization and retrieval tasks by addressing unique micro-video challenges.

    More Related Videos

    A Microfluidic Technique to Probe Cell Deformability
    09:47

    A Microfluidic Technique to Probe Cell Deformability

    Published on: September 3, 2014

    11.3K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    Related Experiment Videos

    Last Updated: Jul 5, 2025

    Video-rate Scanning Confocal Microscopy and Microendoscopy
    14:10

    Video-rate Scanning Confocal Microscopy and Microendoscopy

    Published on: October 20, 2011

    27.9K
    A Microfluidic Technique to Probe Cell Deformability
    09:47

    A Microfluidic Technique to Probe Cell Deformability

    Published on: September 3, 2014

    11.3K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Micro-video summarization for retrieval is underexplored.
    • Existing methods for long videos are unsuitable for micro-videos due to unique characteristics like short duration, diverse content, and modality gaps.
    • Effective query generation for micro-video retrieval requires specialized approaches.

    Purpose of the Study:

    • To propose a novel model for query-oriented micro-video summarization.
    • To generate concise summaries that capture the main semantics of micro-videos and are formatted as search queries.
    • To enhance the retrieval of micro-videos through effective query generation.

    Main Methods:

    • Developed a query-oriented micro-video summarization (QMS) model using an encoder-decoder transformer architecture.
    • Employed modal-specific encoders for visual and textual signals, followed by an entity-aware module to identify key entities.
    • Implemented a confidence scoring mechanism to bridge semantic gaps between modalities and a novel strategy for sampling effective queries.

    Main Results:

    • The proposed QMS model significantly outperforms existing methods in micro-video summarization.
    • The model demonstrates superior performance in retrieval tasks compared to state-of-the-art approaches.
    • Experimental results validate the effectiveness of the entity-aware learning and query sampling strategies.

    Conclusions:

    • The QMS model offers a robust solution for query-oriented micro-video summarization.
    • This approach effectively addresses the challenges posed by micro-videos and diverse query expressions.
    • The findings contribute to advancing the field of intelligent video retrieval and summarization.