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 …

The goal of this paper is to review the state-of-the-art progress on visual tracking methods,
classify them into different categories, as well as identify future trends. Visual tracking is a …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

We consider the problem of designing a scene-specific pedestrian detector in a scenario
where we have zero instances of real pedestrian data (ie, no labeled real data or …

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

We present a multiple classifier system for model-free tracking. The tasks of detection
(finding the object of interest), recognition (distinguishing similar objects in a scene), and …

Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation
of the object. Whilst the performance of model-free trackers has recently improved …

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 …

In recent years, active learning has emerged as a powerful tool in building robust systems
for object detection using computer vision. Indeed, active learning approaches to on-road …

The performance of a detector depends much on its training dataset and drops significantly
when the detector is applied to a new scene due to the large variations between the source …