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

Encoding01:19

Encoding

241
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
241
Associative Learning01:27

Associative Learning

546
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
546
Association Areas of the Cortex01:21

Association Areas of the Cortex

6.1K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
6.1K
Storage01:23

Storage

128
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
128
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

286
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
286
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.4K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.4K

You might also read

Related Articles

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

Sort by
Same author

A Randomized Controlled Trial of Yizhi Kaiqiao Formula Combined With Repetitive Transcranial Magnetic Stimulation on Neurocognitive and Social Outcomes in Preschool Children With Autism Spectrum Disorder.

Developmental neurobiology·2026
Same author

Advancing high-altitude medicine: a model for the future.

Signal transduction and targeted therapy·2026
Same author

<sup>68</sup>Ga-Labeled LLP2A for PET Imaging of Very Late Antigen-4 in Acute Cardiac Rejection.

Molecular pharmaceutics·2026
Same author

Deciphering Object Concepts: Hierarchical Cross-Modal Relational Reasoning for Mining Object-Attribute-Affordance Associations.

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

Validating a gamified size perception task for identifying cognitive profiles in children: a latent profile analysis of executive function and sensory measures.

Frontiers in psychology·2026
Same author

DARS2 serves as an independent prognostic factor and participates in multiple biological processes in bladder urothelial carcinoma.

Translational andrology and urology·2026

Related Experiment Video

Updated: Sep 2, 2025

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

9.1K

HiSA: Hierarchically Semantic Associating for Video Temporal Grounding.

Zhe Xu, Da Chen, Kun Wei

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 1, 2022
    PubMed
    Summary

    Hierarchically Semantic Associating (HiSA) improves video temporal grounding by disentangling video features and connecting inter-video information. This novel approach enhances language-video alignment for more accurate time interval localization.

    More Related Videos

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K

    Related Experiment Videos

    Last Updated: Sep 2, 2025

    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

    9.1K
    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
    07:36

    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

    Published on: November 30, 2018

    15.8K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video Temporal Grounding (VTG) seeks to identify video segments matching textual descriptions.
    • Current VTG methods often struggle with entangled video features and independent instance processing, causing language-video mismatches.
    • Intra-video feature entanglement and inter-video connections are underexplored in existing approaches.

    Purpose of the Study:

    • To propose a novel method, Hierarchically Semantic Associating (HiSA), for precise video-language alignment in VTG.
    • To obtain discriminative video representations for accurate temporal localization.
    • To address limitations of existing VTG methods by considering intra-video and inter-video relationships.

    Main Methods:

    • Disentangling action and background factors from adjacent video segments to reduce intra-video entanglement.
    • Implementing precise multimodal interaction between disentangled video features and language queries.
    • Utilizing cross-guided contrast to capture inter-video connections for improved multimodal understanding.

    Main Results:

    • HiSA significantly outperforms state-of-the-art methods on three benchmark datasets.
    • The method demonstrates enhanced ability in aligning video content with language queries.
    • Improved discriminative representations lead to more accurate temporal localization.

    Conclusions:

    • The proposed Hierarchically Semantic Associating (HiSA) method offers a significant advancement in Video Temporal Grounding.
    • Addressing intra-video entanglement and inter-video connections is crucial for accurate VTG.
    • HiSA provides a robust framework for precise video-language understanding and temporal localization.