Relationship-Embedded Representation Learning for Grounding Referring Expressions.

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Grounding referring expressions in images requires joint understanding of language and vision.
• Existing methods struggle with accurately extracting multi-order relationships between expressions and image contexts.

Purpose of the Study:

• To develop a novel approach for accurately grounding referring expressions by addressing limitations in relationship extraction.
• To enhance cross-modal semantic alignment between language and visual information.

Main Methods:

• Proposed a Cross-Modal Relationship Extractor (CMRE) using cross-modal attention to highlight objects and relationships.
• Introduced a Gated Graph Convolutional Network (GGCN) for fusing and propagating multimodal semantic contexts.
• Represented extracted information as a language-guided visual relation graph.

Main Results:

• The proposed Cross-Modal Relationship Inference Network (CMRE + GGCN) significantly surpasses state-of-the-art methods.
• Demonstrated superior performance on three common benchmark datasets for grounding referring expressions.
• Effectively extracts and aligns multi-order spatial and semantic relationships.

Conclusions:

• The developed network provides a more robust solution for grounding referring expressions.
• Highlights the importance of accurately modeling multi-order relationships for vision-language tasks.
• Offers advancements in human-computer interaction through improved visual grounding.