10,098 research outputs found
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a
universal representation for recognition. However, global CNN activations lack
geometric invariance, which limits their robustness for classification and
matching of highly variable scenes. To improve the invariance of CNN
activations without degrading their discriminative power, this paper presents a
simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
This scheme extracts CNN activations for local patches at multiple scale
levels, performs orderless VLAD pooling of these activations at each level
separately, and concatenates the result. The resulting MOP-CNN representation
can be used as a generic feature for either supervised or unsupervised
recognition tasks, from image classification to instance-level retrieval; it
consistently outperforms global CNN activations without requiring any joint
training of prediction layers for a particular target dataset. In absolute
terms, it achieves state-of-the-art results on the challenging SUN397 and MIT
Indoor Scenes classification datasets, and competitive results on
ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation.Comment: Spotlight at ICCV'1
Learning Fine-grained Image Similarity with Deep Ranking
Learning fine-grained image similarity is a challenging task. It needs to
capture between-class and within-class image differences. This paper proposes a
deep ranking model that employs deep learning techniques to learn similarity
metric directly from images.It has higher learning capability than models based
on hand-crafted features. A novel multiscale network structure has been
developed to describe the images effectively. An efficient triplet sampling
algorithm is proposed to learn the model with distributed asynchronized
stochastic gradient. Extensive experiments show that the proposed algorithm
outperforms models based on hand-crafted visual features and deep
classification models.Comment: CVPR 201
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel spherical convolutional network that implements exact
convolutions on the sphere by realizing them in the spherical harmonic domain.
Resulting filters have local symmetry and are localized by enforcing smooth
spectra. We apply a novel pooling on the spectral domain and our operations are
independent of the underlying spherical resolution throughout the network. We
show that networks with much lower capacity and without requiring data
augmentation can exhibit performance comparable to the state of the art in
standard retrieval and classification benchmarks.Comment: Camera-ready. Accepted to ECCV'18 as oral presentatio
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