The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

This paper investigates long-term tracking of unknown objects in a video stream. The object
is defined by its location and extent in a single frame. In every frame that follows, the task is …

This paper shows that the performance of a binary classifier can be significantly improved by
the processing of structured unlabeled data, ie data are structured if knowing the label of …

Boosting has become very popular in computer vision, showing impressive performance in
detection and recognition tasks. Mainly off-line training methods have been used, which …

Online learning has shown to be successful in tracking-by-detection of previously unknown
objects. However, most approaches are limited to a bounding-box representation with fixed …

Robust and efficient target-tracking algorithms embedded on moving platforms, are a
requirement for many computer vision and robotic applications. However, deployment of a …

In recent years significant progress has been made learning generic pedestrian detectors
from manually labeled large scale training sets. However, when a generic pedestrian …

This paper treats tracking as a foreground/background classification problem and proposes
an online semi-supervised learning framework. Initialized with a small number of labeled …

Visual tracking is a challenging problem, as an object may change its appearance due to
viewpoint variations, illumination changes, and occlusion. Also, an object may leave the field …

The performance of a generic pedestrian detector may drop significantly when it is applied to
a specific scene due to the mismatch between the source training set and samples from the …