Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Normal and Tangetial Components: Problem Solving

Normal and Tangetial Components: Problem Solving

Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².

You might also read

Related Articles

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

Sort by

Same author

Decoding the structure of GPR151 via NELiS.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Chaperonin TRiC bridges radial spokes for folding locally translated proteins to sustain mammalian sperm flagellar motility.

Molecular cell·2026

Same author

Structural insight into the assembly and D antigenicity of polio type 1 stabilized virus-like particles.

NPJ vaccines·2026

Same author

PDCD5 promotes substrate release from the TRiC complex in cilia and flagella.

Molecular cell·2026

Same author

Integrated Whole-Transcriptome Analysis to Elucidate the Core Regulatory Network of circRNA Involved in Ovarian Development and Reproductive Capacity Differences in Sheep: circRNA2058-miR-9226-5p-<i>MET</i> Axis.

Animals : an open access journal from MDPI·2025

Same author

An anti-CD47 antibody binds to a distinct epitope in a novel metal ion-dependent manner to minimize cross-linking of red blood cells.

The Journal of biological chemistry·2025

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

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

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

A visual question answering method based on task decomposition.

Yao Cong¹, Hongwei Mo¹

¹College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, China.

|November 13, 2025

Summary

This summary is machine-generated.

This study introduces a Graph-to-Sequence Task Decomposition Network (Graph2Seq-TDN) for visual question answering (VQA). The novel method enhances natural language understanding for accurate task decomposition and improved reasoning execution, outperforming traditional approaches.

More Related Videos

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Related Experiment Videos

Last Updated: Jan 11, 2026

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

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

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Area of Science:

Computer Vision
Natural Language Processing
Artificial Intelligence

Background:

Visual Question Answering (VQA) integrates computer vision and natural language processing to assess visual reasoning.
Traditional VQA methods often rely on multimodal fusion, which can be prone to data bias and lack interpretability.
Task decomposition VQA methods offer better interpretability and reduced bias but struggle with flexible natural language parsing.

Purpose of the Study:

To improve the accuracy of task decomposition in VQA by leveraging semantic structural information from natural language.
To enhance the reasoning execution performance in VQA systems.
To develop a novel VQA model that overcomes limitations of existing task decomposition approaches.

Main Methods:

Propose a Graph-to-Sequence Task Decomposition Network (Graph2Seq-TDN) utilizing semantic graph structures for natural language parsing.
Implement a novel reasoning executor to augment symbolic reasoning capabilities.
Validate the model on diverse datasets including CLEVR, CLEVR-Human, CLEVR-CoGenT, and GQA.

Main Results:

The Graph2Seq-TDN model demonstrated superior answering accuracy compared to baseline methods.
Program accuracy was significantly improved by the proposed task decomposition approach.
The model achieved better performance with reduced training costs under equivalent accuracy.

Conclusions:

Task decomposition guided by semantic structural information offers a promising direction for VQA.
The Graph2Seq-TDN model effectively addresses challenges in natural language parsing for VQA task decomposition.
The proposed reasoning executor enhances overall VQA performance and efficiency.