16,542 research outputs found
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.Comment: CVPR 201
Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes
Identifying important biomarkers that are predictive for cancer patients'
prognosis is key in gaining better insights into the biological influences on
the disease and has become a critical component of precision medicine. The
emergence of large-scale biomedical survival studies, which typically involve
excessive number of biomarkers, has brought high demand in designing efficient
screening tools for selecting predictive biomarkers. The vast amount of
biomarkers defies any existing variable selection methods via regularization.
The recently developed variable screening methods, though powerful in many
practical setting, fail to incorporate prior information on the importance of
each biomarker and are less powerful in detecting marginally weak while jointly
important signals. We propose a new conditional screening method for survival
outcome data by computing the marginal contribution of each biomarker given
priorly known biological information. This is based on the premise that some
biomarkers are known to be associated with disease outcomes a priori. Our
method possesses sure screening properties and a vanishing false selection
rate. The utility of the proposal is further confirmed with extensive
simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL)
dataset.Comment: 34 pages, 3 figure
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