34,968 research outputs found
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Observation of vacancy-induced suppression of electronic cooling in defected graphene
Previous studies of electron-phonon interaction in impure graphene have found
that static disorder can give rise to an enhancement of electronic cooling. We
investigate the effect of dynamic disorder and observe over an order of
magnitude suppression of electronic cooling compared with clean graphene. The
effect is stronger in graphene with more vacancies, confirming its
vacancy-induced nature. The dependence of the coupling constant on the phonon
temperature implies its link to the dynamics of disorder. Our study highlights
the effect of disorder on electron-phonon interaction in graphene. In addition,
the suppression of electronic cooling holds great promise for improving the
performance of graphene-based bolometer and photo-detector devices.Comment: 13 pages, 4 figure
IMPACT OF THE IMPROVEMENT AND HIGH-QUALITY DEVELOPMENT OF INTELLIGENT LOGISTICS INFORMATION SYSTEM ON PEOPLE WITH COMMUNICATION AND ADAPTATION BARRIERS
IMPACT OF THE IMPROVEMENT AND HIGH-QUALITY DEVELOPMENT OF INTELLIGENT LOGISTICS INFORMATION SYSTEM ON PEOPLE WITH COMMUNICATION AND ADAPTATION BARRIERS
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