Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s
Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …
Large selective kernel network for remote sensing object detection
Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
Eva-02: A visual representation for neon genesis
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained
to reconstruct strong and robust language-aligned vision features via masked image …
to reconstruct strong and robust language-aligned vision features via masked image …
Hiera: A hierarchical vision transformer without the bells-and-whistles
Modern hierarchical vision transformers have added several vision-specific components in
the pursuit of supervised classification performance. While these components lead to …
the pursuit of supervised classification performance. While these components lead to …
Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model
I Pacal - Expert Systems with Applications, 2024 - Elsevier
The timely identification of diseases in maize leaf offers several benefits such as increased
crop productivity, reduced reliance on harmful chemicals, and improved production of …
crop productivity, reduced reliance on harmful chemicals, and improved production of …
A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
NTIRE 2023 challenge on stereo image super-resolution: Methods and results
In this paper, we summarize the 2nd NTIRE challenge on stereo image super-resolution
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …
(SR) with a focus on new solutions and results. The task of the challenge is to super-resolve …
Firerisk: A remote sensing dataset for fire risk assessment with benchmarks using supervised and self-supervised learning
S Shen, S Seneviratne, X Wanyan… - … Conference on Digital …, 2023 - ieeexplore.ieee.org
In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
Masked modeling for self-supervised representation learning on vision and beyond
As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …
increasing attention in recent years thanks to its remarkable representation learning ability …
Dreamteacher: Pretraining image backbones with deep generative models
In this work, we introduce a self-supervised feature representation learning framework
DreamTeacher that utilizes generative networks for pre-training downstream image …
DreamTeacher that utilizes generative networks for pre-training downstream image …