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Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning
1Department of Computer Science, Kyonggi University, Suwon-si 16227, Korea.
Sensors (Basel, Switzerland)
|April 30, 2021
Summary
This study introduces a new Vision-Language-Knowledge Co-embedding (ViLaKC) model for visual commonsense reasoning. The ViLaKC model effectively integrates external knowledge graphs to improve image and text understanding for answering questions.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
Background:
- Visual commonsense reasoning requires solving knowledge acquisition and multimodal alignment.
- Existing models face challenges in integrating external knowledge effectively.
Purpose of the Study:
- To propose a novel Vision-Language-Knowledge Co-embedding (ViLaKC) model.
- To enhance visual commonsense reasoning by incorporating external knowledge graphs.
Main Methods:
- The ViLaKC model extracts relevant knowledge graphs from ConceptNet.
- It employs a pretrained vision-language-knowledge embedding module.
- Graph convolutional neural networks and multi-head self-attention layers are used for co-embedding multimodal data.
Main Results:
- The ViLaKC model demonstrates effectiveness and strong performance.
- Experimental validation was conducted using the VCR v1.0 benchmark dataset.
Conclusions:
- The proposed ViLaKC model offers a robust approach to visual commonsense reasoning.
- Integrating external knowledge graphs significantly improves reasoning capabilities.
Keywords:
graph convolutional networkknowledge graphmultimodal co-embeddingpretrained multi-head self-attention networkvisual commonsense reasoningMore Related Videos
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