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

Retrieval01:12

Retrieval

456
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
456
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

432
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
432
ER Retrieval Pathway01:45

ER Retrieval Pathway

4.9K
In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
4.9K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

507
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
507
Drug Distribution: Volume of Distribution01:25

Drug Distribution: Volume of Distribution

7.6K
The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.
7.6K
F Distribution01:19

F Distribution

10.7K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
10.7K

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Consistency Learning for Test-Time Adaptation in Camouflage Perception.

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

Knowledge Diffusion-Based Adaptive Alignment with Hierarchical Context for Video Temporal Grounding.

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

OmniCharacter++: Towards Comprehensive Benchmark for Realistic Role-Playing Agents.

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

Vision-Language Collaborative Representation Learning for Action Quality Assessment.

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

From Channel Bias to Feature Redundancy: Uncovering the "Less Is More" Principle in Few-Shot Learning.

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

SeMv-3D: Toward Concurrency of Semantic and Multi-View Consistency in General Text-to-3D Generation.

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

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

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

Related Experiment Video

Updated: Feb 15, 2026

Retrieval of Mouse Oocytes
08:42

Retrieval of Mouse Oocytes

Published on: April 28, 2007

28.6K

Distribution-to-Points Matching for Image Text Retrieval.

Zheng Wang, Xing Xu, Lei Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 13, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Distribution-to-Points (D2P) mechanism for image-text retrieval, effectively addressing the one-to-many correspondence challenge by modeling semantic relationships beyond ground-truth instances.

    More Related Videos

    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
    10:14

    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

    Published on: September 2, 2020

    5.5K
    Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
    08:06

    Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

    Published on: June 18, 2018

    7.7K

    Related Experiment Videos

    Last Updated: Feb 15, 2026

    Retrieval of Mouse Oocytes
    08:42

    Retrieval of Mouse Oocytes

    Published on: April 28, 2007

    28.6K
    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
    10:14

    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

    Published on: September 2, 2020

    5.5K
    Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
    08:06

    Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

    Published on: June 18, 2018

    7.7K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Image-text retrieval aims to bridge semantic gaps between modalities.
    • Existing methods often overlook semantically similar but unlabeled instances, leading to one-to-many correspondence issues.
    • Current solutions, primarily based on uncertainty learning, have limited exploration of this one-to-many correspondence.

    Purpose of the Study:

    • To develop a novel Distribution-to-Points (D2P) matching mechanism for image-text retrieval.
    • To capture the one-to-many correspondence between multiple samples and a query using hypergraph modeling.
    • To improve retrieval accuracy by considering semantic multiplicity beyond ground-truth instances.

    Main Methods:

    • Mapping queries to probabilistic embeddings using Mahalanobis distance to learn semantic distributions.
    • Modeling candidate instances as hypergraph nodes and queries as hyperedges to capture correlations.
    • Employing an energy-based framework to align similar candidates and separate dissimilar ones.
    • Implementing distribution-to-points matching based on Mahalanobis distance similarity, accounting for semantic variance.

    Main Results:

    • The D2P mechanism effectively captures one-to-many correspondence in image-text retrieval.
    • Experimental results demonstrate superiority over baseline methods on multiple datasets and metrics.
    • The approach enhances retrieval ability, including ground-truth matching and semantic multiplicity.

    Conclusions:

    • The proposed D2P matching mechanism offers a robust solution for image-text retrieval by addressing the one-to-many correspondence problem.
    • Hypergraph modeling and energy-based semantic frameworks enable comprehensive semantic correlation capture.
    • The method significantly improves retrieval performance, highlighting the importance of considering semantic variance and multiplicity.