Convolutional neural network: a review of models, methodologies and applications to object detection
A Dhillon, GK Verma - Progress in Artificial Intelligence, 2020 - Springer
Deep learning has developed as an effective machine learning method that takes in
numerous layers of features or representation of the data and provides state-of-the-art …
numerous layers of features or representation of the data and provides state-of-the-art …
Federated learning in mobile edge networks: A comprehensive survey
In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …
Fine-grained image analysis with deep learning: A survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
[HTML][HTML] Cpt: Colorful prompt tuning for pre-trained vision-language models
Abstract Vision-Language Pre-training (VLP) models have shown promising capabilities in
grounding natural language in image data, facilitating a broad range of cross-modal tasks …
grounding natural language in image data, facilitating a broad range of cross-modal tasks …
Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images
A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and
has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread …
has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread …
Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Our work focuses on tackling the challenging but natural visual recognition task of long-
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …
A survey of deep learning-based object detection
Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …
which has been widely applied in people's life, such as monitoring security, autonomous …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
[HTML][HTML] Attention gated networks: Learning to leverage salient regions in medical images
We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …
Understanding deep learning techniques for image segmentation
The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …
based approaches. Many challenging computer vision tasks, such as detection, localization …