  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.
