Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

We review the existing literature on generating text from visual data under the cross-modal
generation umbrella, which affords us to compare and contrast various approaches taking …

In this paper we are interested in the problem of image segmentation given natural
language descriptions, ie referring expressions. Existing works tackle this problem by first …

The existing image captioning approaches typically train a one-stage sentence decoder,
which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage …

Rapid growth of multi-modal documents on the Internet makes multi-modal summarization
research necessary. Most previous research summarizes texts or images separately. Recent …

Image captioning is one of the most challenging tasks in AI because it requires an
understanding of both complex visuals and natural language. Because image captioning is …

Image captioning is a difficult problem for machine learning algorithms to compress huge
amounts of images into descriptive languages. The recurrent models are popularly used as …

Image captioning is a multimodal task involving computer vision and natural language
processing, where the goal is to learn a map** from the image to its natural language …

The task of image difference captioning aims at locating changed objects in similar image
pairs and describing the difference with natural language. The key challenges of this task …

Natural language problems have already been investigated for around five years. Recent
progress in artificial intelligence (AI) has greatly improved the performance of models …