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.

Visual grounding aims to localize an object in an image referred to by a textual query phrase. Various visual grounding approaches have been proposed, and the problem can be modularized into a general framework: proposal generation, multimodal feature representation, and proposal ranking. Of these three modules, most existing approaches focus on the latter two, with the importance of proposal generation generally neglected. In this paper, we rethink the problem of what properties make a good proposal generator. We introduce the diversity and discrimination simultaneously when generating proposals, and in doing so propose Diversified and Discriminative Proposal Networks model (DDPN). Based on the proposals generated by DDPN, we propose a high performance baseline model for visual grounding and evaluate it on four benchmark datasets. Experimental results demonstrate that our model delivers significant improvements on all the tested data-sets (e.g., 18.8% improvement on ReferItGame and 8.2% improvement on Flickr30k Entities over the existing stateof-the-arts respectively).

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Phrase-based visual grounding aims to localise the object in the image referred by a textual query phrase. Most existing approaches adopt a two-stage mechanism to address this problem: first, an off-the-shelf proposal generation model is adopted to extract region-based visual features, and then a deep model is designed to score the proposals based on the query phrase and extracted visual features. In contrast to that, the authors design an end-to-end approach to tackle the visual grounding problem in this study. They use a region proposal network to generate object proposals and the corresponding visual features simultaneously, and multi-modal factorised bilinear pooling model to fuse the multi-modal features effectively. After that, two novel losses are posed on top of the multi-modal features to rank and refine the proposals, respectively. To verify the effectiveness of the proposed approach, the authors conduct experiments on three real-world visual grounding datasets, namely Flickr-30k Entities, ReferItGame and RefCOCO. The experimental results demonstrate the significant superiority of the proposed method over the existing state-of-the-arts.