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 …
Object detection in 20 years: A survey
Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …
vision, has received great attention in recent years. Over the past two decades, we have …
UIU-Net: U-Net in U-Net for infrared small object detection
Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …
classification backbone network. This tends to result in tiny object loss and feature …
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 …
Dynamic head: Unifying object detection heads with attentions
The complex nature of combining localization and classification in object detection has
resulted in the flourished development of methods. Previous works tried to improve the …
resulted in the flourished development of methods. Previous works tried to improve the …
Swin transformer: Hierarchical vision transformer using shifted windows
This paper presents a new vision Transformer, called Swin Transformer, that capably serves
as a general-purpose backbone for computer vision. Challenges in adapting Transformer …
as a general-purpose backbone for computer vision. Challenges in adapting Transformer …
No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects
Convolutional neural networks (CNNs) have made resounding success in many computer
vision tasks such as image classification and object detection. However, their performance …
vision tasks such as image classification and object detection. However, their performance …
You only look one-level feature
This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out
that the success of FPN is due to its divide-and-conquer solution to the optimization problem …
that the success of FPN is due to its divide-and-conquer solution to the optimization problem …
Embracing single stride 3d object detector with sparse transformer
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to
input scene size is significantly smaller compared to 2D detection cases. Overlooking this …
input scene size is significantly smaller compared to 2D detection cases. Overlooking this …
A normalized Gaussian Wasserstein distance for tiny object detection
Detecting tiny objects is a very challenging problem since a tiny object only contains a few
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …