Vision-to-Language Tasks Based on Attributes and Attention Mechanism

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Vision-to-language tasks integrate computer vision and natural language processing.
• Existing methods often overlook high-level semantic concepts and region-language relationships.
• A semantic gap persists between visual information and linguistic representation.

Purpose of the Study:

• To reduce the semantic gap in vision-to-language tasks.
• To improve the integration of spatial information with linguistic elements.
• To enhance the performance of image captioning and visual question answering.

Main Methods:

• Development of a two-level attention network architecture.
• Implementation of text-guided attention to identify text-relevant image regions.
• Integration of semantic-guided attention (SA) to highlight concepts and their corresponding regions.

Main Results:

• The proposed method effectively identifies correlated spatial information.
• Experimental results demonstrate excellent performance in image captioning.
• The approach shows superior performance in visual question answering tasks.

Conclusions:

• The novel attention mechanisms significantly improve vision-to-language understanding.
• The method successfully bridges the semantic gap between vision and language.
• This approach offers a promising direction for advanced multimodal AI systems.