DualVD

DualVD encodes images into dual visual and semantic representations and adaptively selects question-relevant information to support multi-round Visual Dialogue.

Key Features:

• Dual-View Image Representation: Represents images from a visual view that captures appearance-level information (objects and visual relationships) and a semantic view that captures high-level visual semantics across the entire image and local regions.
• Adaptive Information Selection: Hierarchically and adaptively selects question-relevant information from the dual views to focus on pertinent aspects across dialogue rounds.
• Integration with Frameworks: Integrated into models based on Late Fusion and Memory Network frameworks and reported state-of-the-art performance across three benchmark datasets.
• Interpretability via Gate Visualization: Provides explicit visualization of gate values to determine the relative contributions of visual and semantic modalities for specific questions.
• Multi-round Visual Dialogue Handling: Designed for multi-round interactions that explore objects, relationships, and high-level semantics, distinguishing it from single-question Visual Question Answering tasks.

Scientific Applications:

• Visual Dialogue Research: Enables research on multi-round image understanding and context-dependent question answering in Visual Dialogue tasks.
• Representation Learning: Supports development of semantic-rich image representations that combine appearance and high-level semantics.
• Machine Learning for Visual Interaction: Improves machine learning models for complex visual interactions by providing modality-aware information selection mechanisms.
• Human–Computer Interaction: Advances context-aware response generation in interactive AI systems by clarifying modality contributions through gate visualization.

Methodology:

Computational steps explicitly include dual visual and semantic image representation, hierarchical adaptive selection of question-relevant information, integration into Late Fusion and Memory Network frameworks, and explicit gate-value visualization.