8,838 research outputs found
EntiTables: Smart Assistance for Entity-Focused Tables
Tables are among the most powerful and practical tools for organizing and
working with data. Our motivation is to equip spreadsheet programs with smart
assistance capabilities. We concentrate on one particular family of tables,
namely, tables with an entity focus. We introduce and focus on two specific
tasks: populating rows with additional instances (entities) and populating
columns with new headings. We develop generative probabilistic models for both
tasks. For estimating the components of these models, we consider a knowledge
base as well as a large table corpus. Our experimental evaluation simulates the
various stages of the user entering content into an actual table. A detailed
analysis of the results shows that the models' components are complimentary and
that our methods outperform existing approaches from the literature.Comment: Proceedings of the 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR '17), 201
Real-time deep hair matting on mobile devices
Augmented reality is an emerging technology in many application domains.
Among them is the beauty industry, where live virtual try-on of beauty products
is of great importance. In this paper, we address the problem of live hair
color augmentation. To achieve this goal, hair needs to be segmented quickly
and accurately. We show how a modified MobileNet CNN architecture can be used
to segment the hair in real-time. Instead of training this network using large
amounts of accurate segmentation data, which is difficult to obtain, we use
crowd sourced hair segmentation data. While such data is much simpler to
obtain, the segmentations there are noisy and coarse. Despite this, we show how
our system can produce accurate and fine-detailed hair mattes, while running at
over 30 fps on an iPad Pro tablet.Comment: 7 pages, 7 figures, submitted to CRV 201
Flow-based Influence Graph Visual Summarization
Visually mining a large influence graph is appealing yet challenging. People
are amazed by pictures of newscasting graph on Twitter, engaged by hidden
citation networks in academics, nevertheless often troubled by the unpleasant
readability of the underlying visualization. Existing summarization methods
enhance the graph visualization with blocked views, but have adverse effect on
the latent influence structure. How can we visually summarize a large graph to
maximize influence flows? In particular, how can we illustrate the impact of an
individual node through the summarization? Can we maintain the appealing graph
metaphor while preserving both the overall influence pattern and fine
readability?
To answer these questions, we first formally define the influence graph
summarization problem. Second, we propose an end-to-end framework to solve the
new problem. Our method can not only highlight the flow-based influence
patterns in the visual summarization, but also inherently support rich graph
attributes. Last, we present a theoretic analysis and report our experiment
results. Both evidences demonstrate that our framework can effectively
approximate the proposed influence graph summarization objective while
outperforming previous methods in a typical scenario of visually mining
academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM),
Shen Zhen, China, December 201
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