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arXiv · 1708.01336

MemexQA: Visual Memex Question Answering

Lu Jiang·Junwei Liang·Liangliang Cao·Yannis Kalantidis·Sachin Farfade·Alexander Hauptmann
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Abstract

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection. Towards solving the task, we 1) present the MemexQA dataset, a large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network architecture for image, text and video question answering. Experimental results on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines and yields the state-of-the-art on this novel and challenging task. The promising results on TextQA and VideoQA suggest MemexNet's efficacy and scalability across various QA tasks.

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Lu Jiang, Junwei Liang, Liangliang Cao, Yannis Kalantidis, Sachin Farfade, Alexander Hauptmann. 2017-08-04. MemexQA: Visual Memex Question Answering. https://arxiv.org/abs/1708.01336

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