Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks
Sign Up to like & getrecommendations! Published in 2019 at "Information Systems Frontiers"
DOI: 10.1007/s10796-018-9850-y
Abstract: Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Many feature embedding algorithms exist, but they often suffer from several… read more here.
Keywords: supervised unsupervised; learning tasks; feature; online learning ... See more keywords
Building segmentation through a gated graph convolutional neural network with deep structured feature embedding
Sign Up to like & getrecommendations! Published in 2020 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2019.11.004
Abstract: Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks… read more here.
Keywords: network; graph convolutional; segmentation; convolutional neural ... See more keywords
A jointly learned deep embedding for person re-identification
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.11.010
Abstract: Abstract Person re-identification (ReID) is an importance study issue in the modern video surveillance area. However, it is very challenging due to the large variations of intra-class and the small variations of inter-class. To solve… read more here.
Keywords: person identification; triplet; identification; feature embedding ... See more keywords
Supervised Deep Feature Embedding With Handcrafted Feature
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2019.2901407
Abstract: Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can… read more here.
Keywords: deep feature; feature; image; handcrafted feature ... See more keywords
Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2022.3163571
Abstract: Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on learning deep neural… read more here.
Keywords: neural networks; feature embedding; correlation; deep feature ... See more keywords
Semantic-Aligned Attention With Refining Feature Embedding for Few-Shot Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2021.3127632
Abstract: Autonomous driving relies on trusty visual recognition of surrounding objects. Few-shot image classification is used in autonomous driving to help recognize objects that are rarely seen. Successful embedding and metric-learning approaches to this task normally… read more here.
Keywords: classification; shot image; feature embedding; feature ... See more keywords