92,403 research outputs found
Cue validity and object-based attention
In a previous study, Egly, Driver, and Rafal (1994) observed both space- and object-based components of visual selective attention. However, the mechanisms underlying these two components and the relationship between them are not well understood. In the present research, with a similar paradigm, these issues were addressed by manipulating cue validity. Behavioral results indicated the presence of both space- and object-based components under high cue validity, similar to the results of Egly et al.'s study. In addition, under low cue validity, the space-based component was absent, whereas the object-based component was maintained. Further event-related potential results demonstrated an object-based effect at a sensory level over the posterior areas of brain, and a space-based effect over the anterior region. The present data suggest that the space- and object-based components reflect mainly voluntary and reflexive mechanisms, respectively
New Lepton Family Symmetry and Neutrino Tribimaximal Mixing
The newly proposed finite symmetry Sigma(81) is applied to the problem of
neutrino tribimaximal mixing. The result is more satisfactory than those of
previous models based on A_4 in that the use of auxiliary symmetries (or
mechanisms) may be avoided. Deviations from the tribimaximal pattern are
expected, but because of its basic structure, only tan^2 (theta_12) may differ
significantly from 0.5 (say 0.45) with sin^2 (2 theta_23) remaining very close
to one, and theta_13 very nearly zero.Comment: 8 pages, no figur
Zero-shot keyword spotting for visual speech recognition in-the-wild
Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an end-to-end architecture comprising (a) a state-of-the-art visual
feature extractor based on spatiotemporal Residual Networks, (b) a
grapheme-to-phoneme model based on sequence-to-sequence neural networks, and
(c) a stack of recurrent neural networks which learn how to correlate visual
features with the keyword representation. Different to prior works on KWS,
which try to learn word representations merely from sequences of graphemes
(i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder
model which learns how to map words to their pronunciation. We demonstrate that
our system obtains very promising visual-only KWS results on the challenging
LRS2 database, for keywords unseen during training. We also show that our
system outperforms a baseline which addresses KWS via automatic speech
recognition (ASR), while it drastically improves over other recently proposed
ASR-free KWS methods.Comment: Accepted at ECCV-201
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
In this paper, we introduce the semantic knowledge of medical images from
their diagnostic reports to provide an inspirational network training and an
interpretable prediction mechanism with our proposed novel multimodal neural
network, namely TandemNet. Inside TandemNet, a language model is used to
represent report text, which cooperates with the image model in a tandem
scheme. We propose a novel dual-attention model that facilitates high-level
interactions between visual and semantic information and effectively distills
useful features for prediction. In the testing stage, TandemNet can make
accurate image prediction with an optional report text input. It also
interprets its prediction by producing attention on the image and text
informative feature pieces, and further generating diagnostic report
paragraphs. Based on a pathological bladder cancer images and their diagnostic
reports (BCIDR) dataset, sufficient experiments demonstrate that our method
effectively learns and integrates knowledge from multimodalities and obtains
significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora
Superpixel Convolutional Networks using Bilateral Inceptions
In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201
Finite Symmetry of Leptonic Mass Matrices
We search for possible symmetries present in the leptonic mixing data from
SU(3) subgroups of order up to 511. Theoretical results based on symmetry are
compared with global fits of experimental data in a chi-squared analysis,
yielding the following results. There is no longer a group that can produce all
the mixing data without a free parameter, but a number of them can accommodate
the first or the second column of the mixing matrix. The only group that fits
the third column is . It predicts and
, in good agreement with experimental results.Comment: Version to appear in Physical Review
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