Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Human-centric perceptions (eg, pose estimation, human parsing, pedestrian detection,
person re-identification, etc.) play a key role in industrial applications of visual models. While …

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 …

For many computer vision applications, such as image description and human identification
recognizing the visual attributes of humans is an essential yet challenging problem. Its …

The design of urban spaces that foster sustainable practices requires new analytical and
structural approaches to spatial planning. An appropriate pedestrian network could …

Abstract Pedestrian Attribute Recognition (PAR) is an important task in computer vision
community and plays an important role in practical video surveillance. The goal of this paper …

Recognising semantic pedestrian attributes in surveillance images is a challenging task for
computer vision, particularly when the imaging quality is poor with complex background …

Pedestrian attribute recognition has attracted many attentions due to its wide applications in
scene understanding and person analysis from surveillance videos. Existing methods try to …

Training the multi-label image recognition models with partial labels, in which merely some
labels are known while others are unknown for each image, is a considerably challenging …

Traffic congestion is becoming the issues of the entire globe. This study aims to explore and
review the data mining and machine learning technologies adopted in research and industry …