210 research outputs found
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnetComment: 24 pages, 23 figures. Accepted to CVPR 201
Dimensionality's blessing: Clustering images by underlying distribution
Many high dimensional vector distances tend to a constant. This is typically
considered a negative "contrast-loss" phenomenon that hinders clustering and
other machine learning techniques. We reinterpret "contrast-loss" as a
blessing. Re-deriving "contrast-loss" using the law of large numbers, we show
it results in a distribution's instances concentrating on a thin "hyper-shell".
The hollow center means apparently chaotically overlapping distributions are
actually intrinsically separable. We use this to develop
distribution-clustering, an elegant algorithm for grouping of data points by
their (unknown) underlying distribution. Distribution-clustering, creates
notably clean clusters from raw unlabeled data, estimates the number of
clusters for itself and is inherently robust to "outliers" which form their own
clusters. This enables trawling for patterns in unorganized data and may be the
key to enabling machine intelligence.Comment: Accepted in CVPR 201
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