123,914 research outputs found
Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph to
describe a given video. Previous works on video captioning usually focus on
video clips. They typically treat an entire video as a whole and generate the
caption conditioned on a single embedding. On the contrary, we consider videos
with rich temporal structures and aim to generate paragraph descriptions that
can preserve the story flow while being coherent and concise. Towards this
goal, we propose a new approach, which produces a descriptive paragraph by
assembling temporally localized descriptions. Given a video, it selects a
sequence of distinctive clips and generates sentences thereon in a coherent
manner. Particularly, the selection of clips and the production of sentences
are done jointly and progressively driven by a recurrent network -- what to
describe next depends on what have been said before. Here, the recurrent
network is learned via self-critical sequence training with both sentence-level
and paragraph-level rewards. On the ActivityNet Captions dataset, our method
demonstrated the capability of generating high-quality paragraph descriptions
for videos. Compared to those by other methods, the descriptions produced by
our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201
The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models
Visual storytelling is an intriguing and complex task that only recently
entered the research arena. In this work, we survey relevant work to date, and
conduct a thorough error analysis of three very recent approaches to visual
storytelling. We categorize and provide examples of common types of errors, and
identify key shortcomings in current work. Finally, we make recommendations for
addressing these limitations in the future.Comment: Accepted to the NAACL 2019 Workshop on Shortcomings in Vision and
Language (SiVL
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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