Simple Baseline for Visual Question Answering

7 Dec 2015  ·  Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus ·

We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset [2], it shows comparable performance to many recent approaches using recurrent neural networks. To explore the strength and weakness of the trained model, we also provide an interactive web demo and open-source code. .

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Visual Question Answering (VQA) COCO Visual Question Answering (VQA) real images 1.0 multiple choice iBOWIMG baseline Percentage correct 62.0 # 10
Visual Question Answering (VQA) COCO Visual Question Answering (VQA) real images 1.0 open ended iBOWIMG baseline Percentage correct 55.9 # 14

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