Zobrazeno 1 - 10
of 31 687
pro vyhledávání: '"Hybrid architecture"'
The field of autonomous navigation for unmanned ground vehicles (UGVs) is in continuous growth and increasing levels of autonomy have been reached in the last few years. However, the task becomes more challenging when the focus is on the exploration
Externí odkaz:
http://arxiv.org/abs/2410.17738
Autor:
Zhu, Xuekang, Ma, Xiaochen, Su, Lei, Jiang, Zhuohang, Du, Bo, Wang, Xiwen, Lei, Zeyu, Feng, Wentao, Pun, Chi-Man, Zhou, Jizhe
The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (mic
Externí odkaz:
http://arxiv.org/abs/2412.13753
Autor:
Campbell, Ryan, Lojo, Nelson, Viswanadha, Kesava, Tryggestad, Christoffer Grondal, Sun, Derrick Han, Vijapurapu, Sriteja, Rolfsen, August, Sahai, Anant
In-Context Learning (ICL) is a phenomenon where task learning occurs through a prompt sequence without the necessity of parameter updates. ICL in Multi-Headed Attention (MHA) with absolute positional embedding has been the focus of more study than ot
Externí odkaz:
http://arxiv.org/abs/2411.03945
Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis
Autor:
Poirier, Fabien
In this paper, we propose a new architecture for real-time anomaly detection in video data, inspired by human behavior combining spatial and temporal analyses. This approach uses two distinct models: (i) for temporal analysis, a recurrent convolution
Externí odkaz:
http://arxiv.org/abs/2410.15909
Autor:
Yu, Zhongkai, Liang, Shengwen, Ma, Tianyun, Cai, Yunke, Nan, Ziyuan, Huang, Di, Song, Xinkai, Hao, Yifan, Zhang, Jie, Zhi, Tian, Zhao, Yongwei, Du, Zidong, Hu, Xing, Guo, Qi, Chen, Tianshi
Publikováno v:
MICRO 2024
Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience while preserving intelligent capabilities. However, such a task exhibit
Externí odkaz:
http://arxiv.org/abs/2409.15654
Widely used traditional pipelines for subcortical brain segmentation are often inefficient and slow, particularly when processing large datasets. Furthermore, deep learning models face challenges due to the high resolution of MRI images and the large
Externí odkaz:
http://arxiv.org/abs/2409.08307
Expanding the long-context capabilities of Multi-modal Large Language Models~(MLLMs) is crucial for video understanding, high-resolution image understanding, and multi-modal agents. This involves a series of systematic optimizations, including model
Externí odkaz:
http://arxiv.org/abs/2409.02889
Autor:
Hou, Guoqiang1 (AUTHOR) hgq15562858033@hrbeu.edu.cn, Yu, Qiwen1 (AUTHOR) yuqiwen@hrbeu.edu.cn, Chen, Fan2 (AUTHOR) chenfan@cqgmy.edu.cn, Chen, Guang1 (AUTHOR) chenguang@hrbeu.edu.cn
Publikováno v:
Mathematics (2227-7390). Dec2024, Vol. 12 Issue 23, p3689. 34p.
This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-classification. Traditional radar-based DOGM often faces challenges in accu
Externí odkaz:
http://arxiv.org/abs/2405.13307
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