Simple Baseline for Visual Question Answering

2015/12/07 by Bolei Zhou, Yuandong Tian, Zhou, Bolei +7 · 31 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Multimodal Machine Learning Applications

paper · pdf · doi:10.48550/arxiv.1512.02167

openalex publication_date 2015/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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. .

Citations

• Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
• Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
• Learning Deep Features for Discriminative Localization
• VQA: Visual Question Answering

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Related

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