An Effective Dense Co-Attention Networks for Visual Question Answering.

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ID: 117416
2020
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Abstract
At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model's basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.
Reference Key
he2020ansensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shirong He, Dezhi Han
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Journal sensors
Year 2020
DOI
E4897
URL
Keywords
Deep learning natural language processing computer vision bi-lstm dense co-attention network visual question answering

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