Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

Marine target detection is a challenging task because degraded underwater images cause
unclear targets. Furthermore, marine targets are small in size and tend to live together. The …

Contemporary person re-identification (re-id) methods mostly compute independentlya
feature representation of each person image in the query set and the gallery set. This …

Attribute-image person re-identification (AIPR) is a cross-modal retrieval task that searches
person images who meet a list of attributes. Due to large modal gaps between attributes and …

Person images captured by unconstrained surveillance cameras often have low resolutions
(LR). This causes the resolution mismatch problem when matched against the high …

Multi-label pedestrian attribute recognition in surveillance is inherently a challenging task
due to poor imaging quality, large pose variations, and so on. In this paper, we improve its …

RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest from
heterogeneous cameras, easily suffering from large image modality discrepancy caused by …

Person re-identification (re-ID) is a method that uses several non-overlapping cameras to
identify the same individual. Person re-ID has been employed successfully in a diversity of …

Person re-identification (re-ID) is commonly investigated as a ranking problem. However, the
performance of existing re-ID models drops dramatically, when they encounter extreme …