[HTML][HTML] A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas
J Terven, DM Córdova-Esparza… - Machine Learning and …, 2023 - mdpi.com
YOLO has become a central real-time object detection system for robotics, driverless cars,
and video monitoring applications. We present a comprehensive analysis of YOLO's …
and video monitoring applications. We present a comprehensive analysis of YOLO's …
Object detection in optical remote sensing images: A survey and a new benchmark
Substantial efforts have been devoted more recently to presenting various methods for
object detection in optical remote sensing images. However, the current survey of datasets …
object detection in optical remote sensing images. However, the current survey of datasets …
Designing network design strategies through gradient path analysis
Designing a high-efficiency and high-quality expressive network architecture has always
been the most important research topic in the field of deep learning. Most of today's network …
been the most important research topic in the field of deep learning. Most of today's network …
nnformer: Volumetric medical image segmentation via a 3d transformer
Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …
from the medical imaging community. Given the ability to exploit long-term dependencies …
TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios
X Zhu, S Lyu, X Wang, Q Zhao - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Object detection on drone-captured scenarios is a recent popular task. As drones always
navigate in different altitudes, the object scale varies violently, which burdens the …
navigate in different altitudes, the object scale varies violently, which burdens the …
nnformer: Interleaved transformer for volumetric segmentation
Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …
from the medical imaging community. Given the ability to exploit long-term dependencies …
Ota: Optimal transport assignment for object detection
Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
A comparative analysis of object detection metrics with a companion open-source toolkit
Recent outstanding results of supervised object detection in competitions and challenges
are often associated with specific metrics and datasets. The evaluation of such methods …
are often associated with specific metrics and datasets. The evaluation of such methods …
[HTML][HTML] Hyper-sausage coverage function neuron model and learning algorithm for image classification
Recently, deep neural networks (DNNs) promote mainly by network architectures and loss
functions; however, the development of neuron models has been quite limited. In this study …
functions; however, the development of neuron models has been quite limited. In this study …
Concealed object detection
We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …
identify objects that are visually embedded in their background. The high intrinsic similarities …