13,443 research outputs found
Recurrent Attentional Networks for Saliency Detection
Convolutional-deconvolution networks can be adopted to perform end-to-end
saliency detection. But, they do not work well with objects of multiple scales.
To overcome such a limitation, in this work, we propose a recurrent attentional
convolutional-deconvolution network (RACDNN). Using spatial transformer and
recurrent network units, RACDNN is able to iteratively attend to selected image
sub-regions to perform saliency refinement progressively. Besides tackling the
scale problem, RACDNN can also learn context-aware features from past
iterations to enhance saliency refinement in future iterations. Experiments on
several challenging saliency detection datasets validate the effectiveness of
RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection
methods.Comment: CVPR 201
Cumulant versus jet-like three-particle correlations
Two-particle jet-like azimuthal correlations have revealed intriguing
modifications to the away-side of high pt trigger particles in relativistic
heavy-ion collisions. Three-particle jet-like azimuthal correlation and
three-particle azimuthal cumulant have been analyzed in experiments in attempt
to distinguish conical emission of jet-correlated particles from other physics
mechanisms. We investigate the difference between three-particle jet-like
correlation and three-particle cumulant in azimuth. We show, under the
circumstance where the away-side two-particle correlation is relatively flat in
azimuth and similar in magnitude to the azimuthal average of the two-particle
correlation signal, that the three-particle cumulant cannot distinguish conical
emission from other physics mechanisms. The three-particle jet-like
correlation, on the other hand, retains its discrimination power.Comment: 13 pages, 8 figures. published versio
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