2019 EMNLP EMNLP 2019

On the Role of Scene Graphs in Image Captioning

Abstract

AbstractScene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context. Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images. However, those graphs introduce noise and it is unclear the effect of parser errors on captioning accuracy. In this work, we investigate to what extent scene graphs can help image captioning. Our results show that a state-of-the-art scene graph parser can boost performance almost as much as the ground truth graphs, showing that the bottleneck currently resides more on the captioning models than on the performance of the scene graph parser.

🌉 Interdisciplinary Bridge — Computer Vision and Knowledge & Reasoning and Natural Language Processing
📈 Trend Setter — Natural Language Generation
🧭 Keyword Pioneer — parser error
🐣 Hot Topic Early Bird — scene graph
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio