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Poster
Visual Reference Resolution using Attention Memory for Visual Dialog
Paul Hongsuck Seo · Andreas Lehrmann · Bohyung Han · Leonid Sigal

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #89
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Visual dialog is a task of answering a series of inter-dependent questions given an input image, and often requires to resolve visual references among the questions. This problem is different from visual question answering (VQA), which relies on spatial attention ({\em a.k.a. visual grounding}) estimated from an image and question pair. We propose a novel attention mechanism that exploits visual attentions in the past to resolve the current reference in the visual dialog scenario. The proposed model is equipped with an associative attention memory storing a sequence of previous (attention, key) pairs. From this memory, the model retrieves previous attention, taking into account recency, that is most relevant for the current question, in order to resolve potentially ambiguous reference(s). The model then merges the retrieved attention with the tentative one to obtain the final attention for the current question; specifically, we use dynamic parameter prediction to combine the two attentions conditioned on the question. Through extensive experiments on a new synthetic visual dialog dataset, we show that our model significantly outperforms the state-of-the-art (by ~16 % points) in the situation where the visual reference resolution plays an important role. Moreover, the proposed model presents superior performance (~2 % points improvement) in the Visual Dialog dataset, despite having significantly fewer parameters than the baselines.

Keywords: Attention Models · Visual Question Answering · Memory-Augmented Neural Networks

Author Information

Paul Hongsuck Seo (POSTECH)
Andreas Lehrmann (Disney Research)
Bohyung Han (Seoul National University)
Leonid Sigal (University of British Columbia)

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