73,926 research outputs found
Multi-task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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