Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks
arXiv:1502.06108
Abstract
Artificial agents today can answer factual questions. But they fall short on questions that require common sense reasoning. Perhaps this is because most existing common sense databases rely on text to learn and represent knowledge. But much of common sense knowledge is unwritten - partly because it tends not to be interesting enough to talk about, and partly because some common sense is unnatural to articulate in text. While unwritten, it is not unseen. In this paper we leverage semantic common sense knowledge learned from images - i.e. visual common sense - in two textual tasks: fill-in-the-blank and visual paraphrasing. We propose to "imagine" the scene behind the text, and leverage visual cues from the "imagined" scenes in addition to textual cues while answering these questions. We imagine the scenes as a visual abstraction. Our approach outperforms a strong text-only baseline on these tasks. Our proposed tasks can serve as benchmarks to quantitatively evaluate progress in solving tasks that go "beyond recognition". Our code and datasets are publicly available.
Cited by in corpus (13)
- VQA: Visual Question Answering
- Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
- Imagination improves Multimodal Translation
- Visual Madlibs: Fill in the blank Image Generation and Question Answering
- Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
- Yin and Yang: Balancing and Answering Binary Visual Questions
- TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
- A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning
- Think Visually: Question Answering through Virtual Imagery
- Leveraging Visual Question Answering for Image-Caption Ranking
- cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
- Know2Look: Commonsense Knowledge for Visual Search
- mAnI: Movie Amalgamation using Neural Imitation