21 research outputs found
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop
Learning by correlation for computer vision applications: from Kernel methods to deep learning
Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications
Curriculum Dropout
Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method.Comment: Accepted at ICCV (International Conference on Computer Vision) 201