In this paper, a novel knowledge distillation (KD)-based pedestrian attribute recognition
(PAR) model is developed, where a multi-label mixed feature learning network (MMFL-Net) …

For many computer vision applications such as image captioning, visual question
answering, and person search, learning discriminative feature representations at both image …

Human attribute analysis is a challenging task in the field of computer vision. One of the
significant difficulties is brought from largely imbalance-distributed data. Conventional …

Unsupervised domain adaptive object detection aims to adapt detectors from a labelled
source domain to an unlabelled target domain. Most existing works take a two-stage strategy …

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 …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

Deep learning based object detection methods have achieved promising performance in
controlled environments. However, these methods lack sufficient capabilities to handle …

Deep neural networks have rapidly become an indispensable tool in many classification
applications. However, the inclusion of deep learning methods in medical diagnostic …

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 …

In recent years, deep learning models have been more and more widely used in various
fields and have become a research hotspot for various tasks in artificial intelligence, but …