Year: 2018
Type: article
Abstract: Grounding language in images has shown it can help improve performance on many image-language tasks. To spur research on this topic, this dissertation introduces a new dataset which provides the ground truth annotations of the location of noun phrase chunks in image captions. I begin by introducing a constituent task termed phrase localization, where the goal is to localize an entity known to exist in an image when provided with a natural language query. To address this task, I introduce a model which learns a set of models, each of which capture a different concept which is useful in our task. These concepts can be predefined, such as attributes gleamed from the adjectives, as well as those which are automatically learned in a single-end-to-end neural network. I also address the more challenging detection style task, where the goal is to localize a phrase and determine if it is associated with an image. Multiple applications of the models presented in this work demonstrate their value beyond the phrase localization task.
Author: Bryan A. Plummer
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2891350675