13,443 research outputs found

    Recurrent Attentional Networks for Saliency Detection

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    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

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    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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