Knowledge-Enriched Attention Network With Group-Wise Semantic for Visual Storytelling.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Visual storytelling is challenging due to limitations in existing methods that focus on explicit image content.
• Current approaches struggle to capture implicit information and holistic dependencies, leading to rigid and incoherent stories.

Purpose of the Study:

• To develop an advanced visual storytelling model capable of generating more imaginative, coherent, and fluent narratives.
• To address the limitations of existing methods in extracting implicit information and maintaining narrative consistency.

Main Methods:

• A novel knowledge-enriched attention network to extract implicit concepts from external knowledge systems.
• A cascade cross-modal attention mechanism for imaginative and concrete representations.
• A group-wise semantic module with second-order pooling for global consistency.
• A unified one-stage encoder-decoder model for end-to-end training and inference.

Main Results:

• The proposed model significantly outperforms state-of-the-art methods in visual storytelling.
• Experiments demonstrate superior performance based on both objective and subjective evaluation metrics.
• The knowledge-enriched attention and group-wise semantic modules contribute to improved narrative quality.

Conclusions:

• The novel knowledge-enriched attention network and group-wise semantic model enhance visual storytelling capabilities.
• The end-to-end trained model generates more reasonable and fluent stories by leveraging implicit information.
• This approach represents a significant advancement in the field of automated visual storytelling.