[HTML][HTML] Deep learning in computer vision: A critical review of emerging techniques and application scenarios
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 …
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
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 …
development of deep convolutional neural networks (CNN). This paper provides a …
Towards large-scale small object detection: Survey and benchmarks
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …
prominent advances in past years. However, such prosperity could not camouflage the …
Ds-transunet: Dual swin transformer u-net for medical image segmentation
Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …
representation learning. Inspired by the success of self-attention mechanism in transformer …
Crossvit: Cross-attention multi-scale vision transformer for image classification
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 …
classification compared to convolutional neural networks. Inspired by this, in this paper, we …
Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation
Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …
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
Remote sensing image change detection (CD) is done to identify desired significant
changes between bitemporal images. Given two co-registered images taken at different …
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
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 …
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 …
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 …
digital image inference. It not only provides the classes of the image objects, but also …