Core-to-Global Reasoning for Compositional Visual Question Answering

Compositional visual question answering (Compositional VQA) needs to provide an answer to a compositional question, which requires the model to have advanced capabilities of multi-modal semantic understanding and logical reasoning. However, current VQA models mainly concentrate on enriching the visual representations of images and neglect the redundancy in the enriched information to bring some negative impacts. To enhance the value and availability of semantic features, we propose a novel core-to-global reasoning (CTGR) model for compositional VQA. The model first extracts both global features and core features from image and question through a feature embedding module. Then, to enhance the value of semantic features, we propose an information filtering module to align visual features and text features at the core semantic level and to filter out the redundancy carried by image and question features at the global semantic level, which can further strengthen cross-modal correlations. Besides, we design a novel core-to-global reasoning mechanism for multimodal fusion, which integrates content features from core learning and context features from global features for accurate answer predictions. Finally, extensive experimental results on GQA, GQA-sub, VQA2.0 and Visual7W demonstrate the effectiveness and superiority of CTGR.