Dependency graph

Question answering

Linguistics

Philosophy

Planar graph

Dual graph

Literature

Theoretical computer science

Computer science

Computer network

Natural language processing

Physics

Graph

Optics

Image (mathematics)

Focus (optics)

Artificial intelligence

Information retrieval

Dependency (UML)

Channel (broadcasting)

Dual (grammatical number)

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.

Aligned Dual Channel Graph Convolutional Network for Visual Question Answering