Boosting is a class of machine learning methods based on the idea that a combination of
simple classifiers (obtained by a weak learner) can perform better than any of the simple …

Adaptive tracking-by-detection methods are widely used in computer vision for tracking
arbitrary objects. Current approaches treat the tracking problem as a classification task and …

Visual object tracking (VOT) is an important subfield of computer vision. It has widespread
application domains, and has been considered as an important part of surveillance and …

We present a semi-supervised approach that localizes multiple unknown object instances in
long videos. We start with a handful of labeled boxes and iteratively learn and label …

While representation learning aims to derive interpretable features for describing visual
data, representation disentanglement further results in such features so that particular image …

In computer vision, image datasets used for classification are naturally associated with
multiple labels and comprised of multiple views, because each image may contain several …

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS)
formulation for the problem of learning an unknown functional dependency between a …

Semisupervised classification (SSC) algorithms use labeled and unlabeled data to predict
labels of unseen instances. Classifier ensembles have been successfully studied and …

Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples,
which are in turn, used for retraining the tracker to localize the target using the collective …

In the most tracking approaches, a score function is utilized to determine which candidate is
the optimal one by measuring the similarity between the candidate and the template …