Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, image, and answer ) is required. Specifically, it is necessary for an agent to 1) understand question-relevant dialog history and 2) focus on question-relevant  visual contents among the diverse visual contents in a given image . In this paper, we propose Multi-View Attention Network (MVAN), which considers complementary views of multimodal inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two different textual-views (i.e., Topic Aggregation and Context Matching), and integrates multimodal representations with two-step fusion process. Experimental results on VisDial v1.0 and v0.9 benchmarks show the effectiveness of our proposed model, which outperforms the previous state-of-the-art methods with respect to all evaluation metrics.
Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image ) is required. Specifically, it is necessary for an agent to 1) determine the semantic intent of question and 2) align question-relevant textual and visual contents among heterogeneous modality inputs . In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment). Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms the previous state-of-the-art methods with respect to all evaluation metrics.
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image, and answer
and image
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understand question-relevant dialog history
determine the semantic intent of question
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focus on
align
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textual and
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the diverse visual contents in a given image
heterogeneous modality inputs
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considers complementary views of multimodal
leverages multiple views about heterogeneous
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different textual-views
complementary modules
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integrates multimodal representations with two-step fusion process.
builds multimodal representations through sequential alignment processes (i.e., Modality Alignment).
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and v0.9 benchmarks
dataset