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 …

Most existing person re-identification (re-id) methods require supervised model learning
from a separate large set of pairwise labelled training data for every single camera pair. This …

Person re-identification (re-ID) and attribute recognition share a common target at learning
pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID …

The visual appearance of a person is easily affected by many factors like pose variations,
viewpoint changes and camera parameter differences. This makes person Re-Identification …

Most existing zero-shot learning approaches exploit transfer learning via an intermediate
semantic representation shared between an annotated auxiliary dataset and a target dataset …

Most existing person re-identification (Re-ID) approaches follow a supervised learning
framework, in which a large number of labelled matching pairs are required for training. This …

Learning semantic attributes for person re-identification and description-based person
search has gained increasing interest due to attributes' great potential as a pose and view …

We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE)
framework for person re-identification. Re-identifications from multiple cameras are regarded …

Most existing person re-identification (Re-ID) methods are based on supervised learning of
a discriminative distance metric. They thus require a large amount of labelled training image …

Person re-identification (Re-ID), aiming to retrieve interested people through multiple non-
overlapping cameras, has caused concerns in pattern recognition communities and …