MemexQA: Visual Memex Question Answering

August 04, 2017 · Entered Twilight · 🏛 arXiv.org

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Repo contents: README.MD, main.py, model.py, preprocess.py, tester.py, trainer.py, utils.py

Authors Lu Jiang, Junwei Liang, Liangliang Cao, Yannis Kalantidis, Sachin Farfade, Alexander Hauptmann arXiv ID 1708.01336 Category cs.CV: Computer Vision Cross-listed cs.CL Citations 28 Venue arXiv.org Repository https://github.com/JunweiLiang/MemexQA_StarterCode ⭐ 4 Last Checked 1 month ago

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.