515 research outputs found
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
An unprecedented booming has been witnessed in the research area of artistic
style transfer ever since Gatys et al. introduced the neural method. One of the
remaining challenges is to balance a trade-off among three critical
aspects---speed, flexibility, and quality: (i) the vanilla optimization-based
algorithm produces impressive results for arbitrary styles, but is
unsatisfyingly slow due to its iterative nature, (ii) the fast approximation
methods based on feed-forward neural networks generate satisfactory artistic
effects but bound to only a limited number of styles, and (iii)
feature-matching methods like AdaIN achieve arbitrary style transfer in a
real-time manner but at a cost of the compromised quality. We find it
considerably difficult to balance the trade-off well merely using a single
feed-forward step and ask, instead, whether there exists an algorithm that
could adapt quickly to any style, while the adapted model maintains high
efficiency and good image quality. Motivated by this idea, we propose a novel
method, coined MetaStyle, which formulates the neural style transfer as a
bilevel optimization problem and combines learning with only a few
post-processing update steps to adapt to a fast approximation model with
satisfying artistic effects, comparable to the optimization-based methods for
an arbitrary style. The qualitative and quantitative analysis in the
experiments demonstrates that the proposed approach achieves high-quality
arbitrary artistic style transfer effectively, with a good trade-off among
speed, flexibility, and quality.Comment: AAAI 2019 spotlight. Supplementary:
http://wellyzhang.github.io/attach/aaai19zhang_supp.pdf GitHub:
https://github.com/WellyZhang/MetaStyle Project:
http://wellyzhang.github.io/project/metastyle.htm
The Influence of Social Comparison and Peer Group Size on Risky Decision-Making
This study explores the influence of different social reference points and different comparison group sizes on risky decision-making. Participants were presented with a scenario describing an exam, and presented with the opportunity of making a risky decision in the context of different information provided about the performance of their peers. We found that behavior was influenced, not only by comparison with peers, but also by the size of the comparison group. Specifically, the larger the reference group, the more polarized the behavior it prompted. In situations describing social loss, participants were led to make riskier decisions after comparing themselves against larger groups, while in situations describing social gain, they become more risk averse. These results indicate that decision making is influenced both by social comparison and the number of people making up the social reference group
Rearrange Indoor Scenes for Human-Robot Co-Activity
We present an optimization-based framework for rearranging indoor furniture
to accommodate human-robot co-activities better. The rearrangement aims to
afford sufficient accessible space for robot activities without compromising
everyday human activities. To retain human activities, our algorithm preserves
the functional relations among furniture by integrating spatial and semantic
co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining
the robot's accessible space by the amount of open space it can traverse and
the number of objects it can reach, we formulate the rearrangement for
human-robot co-activity as an optimization problem, solved by adaptive
simulated annealing (ASA) and covariance matrix adaptation evolution strategy
(CMA-ES). Our experiments on the SUNCG dataset quantitatively show that
rearranged scenes provide an average of 14% more accessible space and 30% more
objects to interact with. The quality of the rearranged scenes is qualitatively
validated by a human study, indicating the efficacy of the proposed strategy.Comment: 7 pages, 7 figures; Accepted by ICRA 202
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