Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering

• Published in: IEEE transaction on neural networks and learning systems 2018-12, Vol.29 (12), p.5947-5959
• Main Authors: Yu, Zhou, Yu, Jun, Xiang, Chenchao, Fan, Jianping, Tao, Dacheng
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Coattention learning; Computational modeling; Correlation; deep learning; Divergence; Feature extraction; Knowledge discovery; Mathematical models; multimodal feature fusion; Natural languages; Neural networks; Representations; State of the art; Task analysis; visual question answering (VQA); Visualization
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2018.2817340

Visual question answering (VQA) is challenging, because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multimodal feature fusion that is able to capture the complex interactions between multimodal features; and 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a "coattention" mechanism is developed using a deep neural network (DNN) architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multimodal feature fusion, a generalized multimodal factorized high-order pooling approach (MFH) is developed to achieve more effective fusion of multimodal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the Kullback-Leibler divergence is used as the loss function to achieve precise characterization of the complex correlations between multiple diverse answers with the same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A DNN architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance. With an ensemble of our MFH models, we achieve the state-of-the-art performance on the large-scale VQA data sets and win the runner-up in VQA Challenge 2017.