4,063 research outputs found
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts
Generating captions for images is a task that has recently received
considerable attention. In this work we focus on caption generation for
abstract scenes, or object layouts where the only information provided is a set
of objects and their locations. We propose OBJ2TEXT, a sequence-to-sequence
model that encodes a set of objects and their locations as an input sequence
using an LSTM network, and decodes this representation using an LSTM language
model. We show that our model, despite encoding object layouts as a sequence,
can represent spatial relationships between objects, and generate descriptions
that are globally coherent and semantically relevant. We test our approach in a
task of object-layout captioning by using only object annotations as inputs. We
additionally show that our model, combined with a state-of-the-art object
detector, improves an image captioning model from 0.863 to 0.950 (CIDEr score)
in the test benchmark of the standard MS-COCO Captioning task.Comment: Accepted at EMNLP 201
The Eurovision St Andrews collection of photographs
This report describes the Eurovision image collection compiled for the ImageCLEF (Cross Language Evaluation Forum) evaluation exercise. The image collection consists of around 30,000 photographs from the collection provided by the University of St Andrews Library. The construction and composition of this unique image collection are described, together with the necessary information to obtain and use the image collection
Evaluation of Automatic Video Captioning Using Direct Assessment
We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics for comparing automatic video captions against a manual caption such as
BLEU and METEOR, drawn from techniques used in evaluating machine translation,
were used in the TRECVid video captioning task in 2016 but these are shown to
have weaknesses. The work presented here brings human assessment into the
evaluation by crowdsourcing how well a caption describes a video. We
automatically degrade the quality of some sample captions which are assessed
manually and from this we are able to rate the quality of the human assessors,
a factor we take into account in the evaluation. Using data from the TRECVid
video-to-text task in 2016, we show how our direct assessment method is
replicable and robust and should scale to where there many caption-generation
techniques to be evaluated.Comment: 26 pages, 8 figure
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
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