Comparative Evaluation of Visual Question Answering with Scene Graph Utilization

Integrating scene graphs enhances Visual Question Answering (VQA) systems' performance in dynamic and diverse real-world applications. This study examines the impact of scene graphs and external knowledge on VQA systems. Using the LLM-based model, LLaVA-v1.5, we trained and evaluated models with and without scene graphs. Additionally, we compared models trained solely with scene graphs to those trained with both scene graphs and external knowledge.

Authors

Institutions

Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/ictc62082.2024.10827158
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative Evaluation of Visual Question Answering with Scene Graph Utilization

Changeun Lee, Choulsoo Jang, Sungwoo Jun, Jaehwan Kim et al.
Multimodal Machine Learning Applications
article

Comparative Evaluation of Visual Question Answering with Scene Graph Utilization

Changeun Lee, Choulsoo Jang, Sungwoo Jun, Jaehwan Kim, Yoon‐Seok Choi, Kwang‐Yong Kim
article en

Abstract

Integrating scene graphs enhances Visual Question Answering (VQA) systems' performance in dynamic and diverse real-world applications. This study examines the impact of scene graphs and external knowledge on VQA systems. Using the LLM-based model, LLaVA-v1.5, we trained and evaluated models with and without scene graphs. Additionally, we compared models trained solely with scene graphs to those trained with both scene graphs and external knowledge.

Electronics and Telecommunications Research Institute (KR)
Defense Acquisition Program Administration
Openalex Percentile: Top 75%
Multimodal Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.