A survey of convolutional neural networks: analysis, applications, and prospects
A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …
learning field. Since CNN made impressive achievements in many areas, including but not …
Deep learning for person re-identification: A survey and outlook
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …
overlapping cameras. With the advancement of deep neural networks and increasing …
Nformer: Robust person re-identification with neighbor transformer
Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …
cameras and scenarios, in which robust and discriminative representation learning is …
Deep learning in multi-object detection and tracking: state of the art
Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …
computer vision, and have been widely applied in various fields, such as health-care …
Joint discriminative and generative learning for person re-identification
Person re-identification (re-id) remains challenging due to significant intra-class variations
across different cameras. Recently, there has been a growing interest in using generative …
across different cameras. Recently, there has been a growing interest in using generative …
Abd-net: Attentive but diverse person re-identification
Attention mechanisms have been found effective for person re-identification (Re-ID).
However, the learned" attentive" features are often not naturally uncorrelated or" diverse" …
However, the learned" attentive" features are often not naturally uncorrelated or" diverse" …
Contrastive adaptation network for unsupervised domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) makes predictions for the target domain
data while manual annotations are only available in the source domain. Previous methods …
data while manual annotations are only available in the source domain. Previous methods …
[HTML][HTML] Deep learning-based image recognition for autonomous driving
H Fujiyoshi, T Hirakawa, T Yamashita - IATSS research, 2019 - Elsevier
Various image recognition tasks were handled in the image recognition field prior to 2010 by
combining image local features manually designed by researchers (called handcrafted …
combining image local features manually designed by researchers (called handcrafted …
TBE-Net: A three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification
Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …
Occlusion aware facial expression recognition using CNN with attention mechanism
Facial expression recognition in the wild is challenging due to various unconstrained
conditions. Although existing facial expression classifiers have been almost perfect on …
conditions. Although existing facial expression classifiers have been almost perfect on …