Visual Relationship Detection with Multimodal Fusion and Reasoning

Area of Science:

• Computer Vision
• Artificial Intelligence
• Knowledge Representation

Background:

• Current visual relationship detection methods rely solely on visual features.
• This approach fails to incorporate common sense reasoning, limiting scene understanding.
• Hidden relationships in complex scenes are often missed by existing models.

Purpose of the Study:

• To develop a unified framework for visual relationship detection.
• To integrate visual features with external common sense knowledge.
• To improve the prediction of hidden relationships in complex visual scenes.

Main Methods:

• Unifying vision-language fusion and knowledge graph reasoning.
• Combining visual feature embedding with common sense knowledge.
• Implementing an object-pair proposal module to manage combinatorial complexity.

Main Results:

• The proposed method demonstrates superior performance compared to state-of-the-art approaches.
• Achieved significant improvements on the Visual Genome dataset.
• Showcased enhanced accuracy on the Visual Relationship Detection dataset.

Conclusions:

• The integration of vision-language fusion and knowledge graph reasoning is effective for visual relationship detection.
• The proposed method successfully addresses limitations of purely visual approaches.
• This work advances the field by enabling more comprehensive visual scene understanding.