Recently, deep learning-based object detection techniques have arisen alongside time-consuming training and data collection challenges. Although few-shot learning techniques can boost models with few samples to lighten the training load, these approaches still need to be improved when applied to remote-sensing images. Objects in remote-sensing images are often small with an uncertain scale. An insufficient amount of samples would further aggravate this issue, leading to poor detection performance. This paper proposes a Gaussian-scale enhancement (GSE) strategy and a multi-branch patch-embedding attention aggregation (MPEAA) module for cross-scale few-shot object detection to address this issue. Our model can enrich the scale information of ...
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve...
Aimed at the problems of small object detection in high resolution remote sensing images, such as di...
The objects in remote sensing images have large-scale variations, arbitrary directions, and are usua...
Few-shot object detection is a recently emerging branch in the field of computer vision. Recent rese...
Recent years have witnessed rapid development and remarkable achievements on deep learning object de...
In this paper, we deal with the problem of object detection on remote sensing images. Previous metho...
Object detection is crucial in aerial imagery analysis. Previous methods based on convolutional neur...
Object detection, which aims to recognize and locate objects within images using bounding boxes, is ...
Accurate detection of multiclass instance objects in remote sensing images (RSIs) is a fundamental b...
With the rapid advances in remote-sensing technologies and the larger number of satellite images, fa...
International audienceObject detection from aerial and satellite remote sensing images has been an a...
International audienceThis article tackles the problem of detecting small objects in satellite or ae...
Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resoluti...
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene ...
The existing object detection algorithm based on the deep convolution neural network needs to carry ...
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve...
Aimed at the problems of small object detection in high resolution remote sensing images, such as di...
The objects in remote sensing images have large-scale variations, arbitrary directions, and are usua...
Few-shot object detection is a recently emerging branch in the field of computer vision. Recent rese...
Recent years have witnessed rapid development and remarkable achievements on deep learning object de...
In this paper, we deal with the problem of object detection on remote sensing images. Previous metho...
Object detection is crucial in aerial imagery analysis. Previous methods based on convolutional neur...
Object detection, which aims to recognize and locate objects within images using bounding boxes, is ...
Accurate detection of multiclass instance objects in remote sensing images (RSIs) is a fundamental b...
With the rapid advances in remote-sensing technologies and the larger number of satellite images, fa...
International audienceObject detection from aerial and satellite remote sensing images has been an a...
International audienceThis article tackles the problem of detecting small objects in satellite or ae...
Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resoluti...
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene ...
The existing object detection algorithm based on the deep convolution neural network needs to carry ...
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve...
Aimed at the problems of small object detection in high resolution remote sensing images, such as di...
The objects in remote sensing images have large-scale variations, arbitrary directions, and are usua...