[HTML][HTML] Deep learning in computer vision: A critical review of emerging techniques and application scenarios

J Chai, H Zeng, A Li, EWT Ngai - Machine Learning with Applications, 2021 - Elsevier
Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

A survey and performance evaluation of deep learning methods for small object detection

Y Liu, P Sun, N Wergeles, Y Shang - Expert Systems with Applications, 2021 - Elsevier
In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

Towards large-scale small object detection: Survey and benchmarks

G Cheng, X Yuan, X Yao, K Yan, Q Zeng… - … on Pattern Analysis …, 2023 - ieeexplore.ieee.org
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Ds-transunet: Dual swin transformer u-net for medical image segmentation

A Lin, B Chen, J Xu, Z Zhang, G Lu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Crossvit: Cross-attention multi-scale vision transformer for image classification

CFR Chen, Q Fan, R Panda - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
The recently developed vision transformer (ViT) has achieved promising results on image
classification compared to convolutional neural networks. Inspired by this, in this paper, we …

Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation

M Heidari, A Kazerouni, M Soltany… - Proceedings of the …, 2023 - openaccess.thecvf.com
Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

A spatial-temporal attention-based method and a new dataset for remote sensing image change detection

H Chen, Z Shi - Remote Sensing, 2020 - mdpi.com
Remote sensing image change detection (CD) is done to identify desired significant
changes between bitemporal images. Given two co-registered images taken at different …

Deep learning for unmanned aerial vehicle-based object detection and tracking: A survey

X Wu, W Li, D Hong, R Tao, Q Du - IEEE Geoscience and …, 2021 - ieeexplore.ieee.org
Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

YOLOv4-5D: An effective and efficient object detector for autonomous driving

Y Cai, T Luan, H Gao, H Wang, L Chen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The use of object detection algorithms has become extremely important in autonomous
vehicles. Object detection at high accuracy and a fast inference speed is essential for safe …

A survey on instance segmentation: state of the art

AM Hafiz, GM Bhat - International journal of multimedia information …, 2020 - Springer
Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …