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

Observational Learning01:12

Observational Learning

202
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
202
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

369
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...
369
Schemas01:42

Schemas

11.7K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
11.7K
Fixed Action Patterns01:06

Fixed Action Patterns

16.0K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
16.0K
Survival Tree01:19

Survival Tree

105
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
105
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

111
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
111

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

Static and dynamic dissolved oxygen distributions in algal-bacterial granular sludge: mapping intragranular oxygen profile and penetration under different oxygenation strategies.

Bioresource technology·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 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: Jul 15, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

Adaptive Fine-Grained Predicates Learning for Scene Graph Generation.

Xinyu Lyu, Lianli Gao, Pengpeng Zeng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 3, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Adaptive Fine-Grained Predicates Learning (FGPL-A) to improve Scene Graph Generation (SGG) by better distinguishing similar predicates. The method significantly enhances SGG model performance on benchmark datasets.

    More Related Videos

    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
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    439

    Related Experiment Videos

    Last Updated: Jul 15, 2025

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.3K
    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
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    439

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Current Scene Graph Generation (SGG) models struggle with predicates that are difficult to distinguish, such as "woman-on/standing on/walking on-beach".
    • General SGG models often predict common predicates, while re-balancing strategies favor less frequent categories, failing to adequately address subtle predicate distinctions.

    Purpose of the Study:

    • To develop a novel approach, Adaptive Fine-Grained Predicates Learning (FGPL-A), inspired by fine-grained image classification, to enhance the differentiation of hard-to-distinguish predicates in SGG.
    • To improve the accuracy and efficiency of SGG models by addressing the limitations of current methods in handling predicate ambiguity.

    Main Methods:

    • Introduction of an Adaptive Predicate Lattice (PL-A) to dynamically identify and correlate difficult predicates based on the model's learning progress.
    • Development of an Adaptive Category Discriminating Loss (CDL-A) and an Adaptive Entity Discriminating Loss (EDL-A) for fine-grained, adaptive supervision during model training.
    • Implementation of a model-agnostic strategy that refines predicate discrimination using mini-batch predictions.

    Main Results:

    • Significant performance boosts on VG-SGG and GQA-SGG datasets, with Mean Recall@100 increasing by up to 175% and 76%, respectively.
    • Establishment of new state-of-the-art performance benchmarks for Scene Graph Generation.
    • Demonstration of the method's practicability through successful application in Sentence-to-Graph Retrieval and Image Captioning tasks.

    Conclusions:

    • The proposed FGPL-A strategy effectively addresses the challenge of hard-to-distinguish predicates in SGG.
    • The adaptive nature of PL-A, CDL-A, and EDL-A ensures balanced and efficient learning, leading to substantial performance improvements.
    • The model-agnostic approach offers a versatile solution applicable to various downstream tasks, enhancing the overall utility of SGG models.