49 research outputs found
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.Comment: To appear in CVPR 201
Reconstructing the possessive inflection of Proto-Zamucoan
[Extract]The Zamucoan family consists of two living languages: Ayoreo (AY) and Chamacoco (CH), spoken in Northern Chaco (between Bolivia and Paraguay) by approximately 4500 and 2000 people, respectively. The Zamucoan family also includes the now extinct Old Zamuco OZ), described in the early 18th century by the Jesuit Father Ignace Chomé (1958 [ante 1745]). The first stable contacts with the Ayoreos began around the half of the last century, whereas the Chamacocos were already in contact with the Western civilization at the turn of the XIX century, thus undergoing the linguistic influence of Spanish and Guaraní. The Zamucoan family is divided into two branches stemming from Proto-Zamucoan (PZ): according to glottochronological computations (Holman et al. 2011; Müller et al. 2013), CH split long ago from OZ and AY, and indeed it only shares 30% of its lexical roots with AY (Bertinetto 2009). This notwithstanding, all three languages present morphosyntactic correspondences, allowing robust diachronic insights (Ciucci 2013; Ciucci & Bertinetto, to appear)
On rare typological features of the Zamucoan languages, in the framework of the Chaco linguistic area
[Extract] The Zamucoan family only includes two surviving endangered languages:Ayoreo (AY) and Chamacoco (CH), spoken in northern Chaco between Bolivia and Paraguay by approximately 4500 and 2000 people, respectively. The Zamucoan family also includes an extinct language, Ancient Zamuco (AZ), described in the 18yh century by the Jesuit Father Ignace Chomé. AZ is very close to AY from the lexical point of view, but shows striking morphosyntactic correspondences with CH; this allows robust diachronic insights (Ciucci 2013; Ciucci & Bertinetto, submitted)
Learning feed-forward one-shot learners
One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn the parameters of a deep model in one shot. We construct the
learner as a second deep network, called a learnet, which predicts the
parameters of a pupil network from a single exemplar. In this manner we obtain
an efficient feed-forward one-shot learner, trained end-to-end by minimizing a
one-shot classification objective in a learning to learn formulation. In order
to make the construction feasible, we propose a number of factorizations of the
parameters of the pupil network. We demonstrate encouraging results by learning
characters from single exemplars in Omniglot, and by tracking visual objects
from a single initial exemplar in the Visual Object Tracking benchmark.Comment: The first three authors contributed equally, and are listed in
alphabetical orde
End-to-end representation learning for Correlation Filter based tracking
The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Correlation Filter, however, have adopted features that were
either manually designed or trained for a different task. This work is the
first to overcome this limitation by interpreting the Correlation Filter
learner, which has a closed-form solution, as a differentiable layer in a deep
neural network. This enables learning deep features that are tightly coupled to
the Correlation Filter. Experiments illustrate that our method has the
important practical benefit of allowing lightweight architectures to achieve
state-of-the-art performance at high framerates.Comment: To appear at CVPR 201