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