Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested