Zobrazeno 1 - 10
of 10 097
pro vyhledávání: '"Transformer Networks"'
Autor:
Gallo, Ignazio, Gatti, Mattia, Landro, Nicola, Loschiavo, Christian, Boschetti, Mirco, La Grassa, Riccardo
Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of w
Externí odkaz:
http://arxiv.org/abs/2412.01944
Autor:
Hammad, A., Nojiri, Mihoko M
In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the performance of sta
Externí odkaz:
http://arxiv.org/abs/2411.11519
Autor:
Cui, Jiahao, Li, Hui, Zhan, Yun, Shang, Hanlin, Cheng, Kaihui, Ma, Yuqi, Mu, Shan, Zhou, Hang, Wang, Jingdong, Zhu, Siyu
Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we int
Externí odkaz:
http://arxiv.org/abs/2412.00733
Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are accurate but computationally intensive. Recent years, machine learning (M
Externí odkaz:
http://arxiv.org/abs/2411.16483
Large Language Models (LLMs) have demonstrated impressive abilities in symbol processing through in-context learning (ICL). This success flies in the face of decades of predictions that artificial neural networks cannot master abstract symbol manipul
Externí odkaz:
http://arxiv.org/abs/2410.17498
Purpose: In sleep medicine, assessing the evolution of a subject's sleep often involves the costly manual scoring of electroencephalographic (EEG) signals. In recent years, a number of Deep Learning approaches have been proposed to automate this proc
Externí odkaz:
http://arxiv.org/abs/2410.19819
Joint communication and sensing (JC\&S) is emerging as a key component in 5G and 6G networks, enabling dynamic adaptation to environmental changes and enhancing contextual awareness for optimized communication. By leveraging real-time environmental d
Externí odkaz:
http://arxiv.org/abs/2410.16303
This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Transformer-based segmentation model that addresses the limitations of tr
Externí odkaz:
http://arxiv.org/abs/2409.12347
Due to the capability of dynamic state space models (SSMs) in capturing long-range dependencies with linear-time computational complexity, Mamba has shown notable performance in NLP tasks. This has inspired the rapid development of Mamba-based vision
Externí odkaz:
http://arxiv.org/abs/2409.09649
Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Embodied AI and traditional sequential data modelling, potentially limit
Externí odkaz:
http://arxiv.org/abs/2409.02669