14 research outputs found
A Survey of Current Datasets for Vision and Language Research
Integrating vision and language has long been a dream in work on artificial
intelligence (AI). In the past two years, we have witnessed an explosion of
work that brings together vision and language from images to videos and beyond.
The available corpora have played a crucial role in advancing this area of
research. In this paper, we propose a set of quality metrics for evaluating and
analyzing the vision & language datasets and categorize them accordingly. Our
analyses show that the most recent datasets have been using more complex
language and more abstract concepts, however, there are different strengths and
weaknesses in each.Comment: To appear in EMNLP 2015, short proceedings. Dataset analysis and
discussion expanded, including an initial examination into reporting bias for
one of them. F.F. and N.M. contributed equally to this wor
Generating Natural Questions About an Image
There has been an explosion of work in the vision & language community during
the past few years from image captioning to video transcription, and answering
questions about images. These tasks have focused on literal descriptions of the
image. To move beyond the literal, we choose to explore how questions about an
image are often directed at commonsense inference and the abstract events
evoked by objects in the image. In this paper, we introduce the novel task of
Visual Question Generation (VQG), where the system is tasked with asking a
natural and engaging question when shown an image. We provide three datasets
which cover a variety of images from object-centric to event-centric, with
considerably more abstract training data than provided to state-of-the-art
captioning systems thus far. We train and test several generative and retrieval
models to tackle the task of VQG. Evaluation results show that while such
models ask reasonable questions for a variety of images, there is still a wide
gap with human performance which motivates further work on connecting images
with commonsense knowledge and pragmatics. Our proposed task offers a new
challenge to the community which we hope furthers interest in exploring deeper
connections between vision & language.Comment: Proceedings of the 54th Annual Meeting of the Association for
Computational Linguistic
Evaluating Text-to-Image Matching using Binary Image Selection (BISON)
Providing systems the ability to relate linguistic and visual content is one
of the hallmarks of computer vision. Tasks such as text-based image retrieval
and image captioning were designed to test this ability but come with
evaluation measures that have a high variance or are difficult to interpret. We
study an alternative task for systems that match text and images: given a text
query, the system is asked to select the image that best matches the query from
a pair of semantically similar images. The system's accuracy on this Binary
Image SelectiON (BISON) task is interpretable, eliminates the reliability
problems of retrieval evaluations, and focuses on the system's ability to
understand fine-grained visual structure. We gather a BISON dataset that
complements the COCO dataset and use it to evaluate modern text-based image
retrieval and image captioning systems. Our results provide novel insights into
the performance of these systems. The COCO-BISON dataset and corresponding
evaluation code are publicly available from \url{http://hexianghu.com/bison/}
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
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