7,378 research outputs found
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
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
To understand diverse natural language commands, virtual assistants today are
trained with numerous labor-intensive, manually annotated sentences. This paper
presents a methodology and the Genie toolkit that can handle new compound
commands with significantly less manual effort. We advocate formalizing the
capability of virtual assistants with a Virtual Assistant Programming Language
(VAPL) and using a neural semantic parser to translate natural language into
VAPL code. Genie needs only a small realistic set of input sentences for
validating the neural model. Developers write templates to synthesize data;
Genie uses crowdsourced paraphrases and data augmentation, along with the
synthesized data, to train a semantic parser. We also propose design principles
that make VAPL languages amenable to natural language translation. We apply
these principles to revise ThingTalk, the language used by the Almond virtual
assistant. We use Genie to build the first semantic parser that can support
compound virtual assistants commands with unquoted free-form parameters. Genie
achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's
generality by showing a 19% and 31% improvement over the previous state of the
art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage
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