Vision transformers for dense prediction: A survey
S Zuo, Y Xiao, X Chang, X Wang - Knowledge-Based Systems, 2022 - Elsevier
Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …
[HTML][HTML] Deep learning-based monocular depth estimation methods—a state-of-the-art review
Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed
problem in computer vision which has been investigated intensively over the past decade …
problem in computer vision which has been investigated intensively over the past decade …
A survey on vision transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …
network mainly based on the self-attention mechanism. Thanks to its strong representation …
A survey on visual transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …
network mainly based on the self-attention mechanism. Thanks to its strong representation …
Towards real-time monocular depth estimation for robotics: A survey
As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …
motion estimation, obstacle avoidance and scene understanding, monocular depth …
[HTML][HTML] Gcndepth: Self-supervised monocular depth estimation based on graph convolutional network
A Masoumian, HA Rashwan, S Abdulwahab… - Neurocomputing, 2023 - Elsevier
Depth estimation is a challenging task of 3D reconstruction to enhance the accuracy sensing
of environment awareness. This work brings a new solution with improvements, which …
of environment awareness. This work brings a new solution with improvements, which …
[HTML][HTML] A novel ResNet101 model based on dense dilated convolution for image classification
Q Zhang - SN Applied Sciences, 2022 - Springer
Image classification plays an important role in computer vision. The existing convolutional
neural network methods have some problems during image classification process, such as …
neural network methods have some problems during image classification process, such as …
On deep learning techniques to boost monocular depth estimation for autonomous navigation
R de Queiroz Mendes, EG Ribeiro… - Robotics and …, 2021 - Elsevier
Inferring the depth of images is a fundamental inverse problem within the field of Computer
Vision since depth information is obtained through 2D images, which can be generated from …
Vision since depth information is obtained through 2D images, which can be generated from …
Fixing defect of photometric loss for self-supervised monocular depth estimation
S Chen, Z Pu, X Fan, B Zou - … on Circuits and Systems for Video …, 2021 - ieeexplore.ieee.org
View-synthesis-based methods have shown very promising results for the task of
unsupervised depth estimation in single images. Most existing approaches synthesize a …
unsupervised depth estimation in single images. Most existing approaches synthesize a …
U-hrnet: delving into improving semantic representation of high resolution network for dense prediction
High resolution and advanced semantic representation are both vital for dense prediction.
Empirically, low-resolution feature maps often achieve stronger semantic representation …
Empirically, low-resolution feature maps often achieve stronger semantic representation …