Object-Difference Attention: A Simple Relational Attention for Visual Question Answering

Attention mechanism has greatly promoted the development of Visual Question Answering (VQA). Attention distribution, which weights differently on objects (such as image regions or bounding boxes) in an image according to their importance for answering a question, plays a crucial role in attention mechanism. Most of the existing work focuses on fusing image features and text features to calculate the attention distribution without comparisons between different image objects. As a major property of attention, selectivity depends on comparisons between different objects. Comparisons provide more information for assigning attentions better. For achieving this, we propose an object-difference attention (ODA) which calculates the probability of attention by implementing difference operator between different image objects in an image under the guidance of questions in hand. Experimental results on three publicly available datasets show our ODA based VQA model achieves the state-of-the-art results. Furthermore, a general form of relational attention is proposed. Besides ODA, several other relational attentions are given. Experimental results show those relational attentions have strengths on different types of questions.