This paper introduces a new method for 3D points cloud registration based on deep learning. The architecture is composed of three distinct blocs: (i) an encoder with a convolutional graph-based descriptor that encodes the immediate neighborhood of each point and an attention mechanism that encodes the variations of the surface normals. Such descrip...
Advancements in deep learning algorithms for medical imaging, combined with the integration of cyberworlds, have shown great promise in providing precise diagnostic results. One area of interest is the application of these advancements in enhancing personalized treatment of gliomas, a particularly challenging type of brain tumor, by providing more ...
Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods on natural images only, yet the transferability of the announced performance is not guaranteed for applications on other kinds of images. We demonstrate this with an in-depth analysis of existing FSOD methods on aerial images and observed a large performance gap compared ...
Published in Journal of Physics: Conference Series
The accuracy and efficiency of object detection is the key technology to spread autonomous driving. In order to address issues such as missed and false detection in traditional target detection algorithms, deep learning technology is used to optimize the ship target detection model as well as the attention mechanisms in this article. Specifically, ...
When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where there is little room for doubt. We propose in this paper a new attention gate based on the use of Chan...
The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic natu...
Large Language Models of code have seen significant jumps in performance recently. However, these jumps tend to accompany a notable and perhaps concerning increase in scale and costs. We contribute an evaluation of prediction performance with respect to model size by assessing the layer-wise progression for language and user-defined elements in cod...
Published in International Journal of Advanced Network, Monitoring and Controls
The traditional rain removal algorithm needs to optimize a large number of parameters, and it is only effective for rain of a specific shape, and the model generalization ability is poor. In recent years, the performance of rain removal methods based on deep learning is better than many traditional methods, but there are problems such as incomplete...
Published in International Journal of Advanced Network, Monitoring and Controls
Aiming at the problems of low object detection accuracy due to complex background and insufficient semantic information of shallow features in the object detection SSD algorithm, this paper improves the existing SSD algorithm. First, the original vgg16 network is replaced by the ResNet50 network, and the residual network structure as well as the Ba...
As an important indicator of biodiversity and ecological environment in a region, the number and distribution of animals has been given more and more attention by agencies such as nature reserves, wetland parks, and animal protection supervision departments. To protect biodiversity, we need to be able to detect and track the movement of animals to ...
