Zobrazeno 1 - 10
of 124 772
pro vyhledávání: '"Man, P. P."'
Autor:
Chan, Man Ho, Lee, Chak Man
Investigating the signals of dark matter annihilation is one of the most popular ways to understand the nature of dark matter. In particular, many recent studies are focussing on using radio data to examine the possible signals of dark matter reveale
Externí odkaz:
http://arxiv.org/abs/2411.17977
With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-mod
Externí odkaz:
http://arxiv.org/abs/2411.19460
We examine a system in which an impurity qubit is immersed in a quasi-two-dimensional dipolar Bose-Einstein condensate whose collective excitations act as a depasing reservoir for the qubit. The relative dipole-dipole interaction strength is estimate
Externí odkaz:
http://arxiv.org/abs/2411.19438
Identifying defects and anomalies in industrial products is a critical quality control task. Traditional manual inspection methods are slow, subjective, and error-prone. In this work, we propose a novel zero-shot training-free approach for automated
Externí odkaz:
http://arxiv.org/abs/2411.19220
In the research area of image super-resolution, Swin-transformer-based models are favored for their global spatial modeling and shifting window attention mechanism. However, existing methods often limit self-attention to non overlapping windows to cu
Externí odkaz:
http://arxiv.org/abs/2411.18003
With the rapid advancements in deep learning, computer vision tasks have seen significant improvements, making two-stream neural networks a popular focus for video based action recognition. Traditional models using RGB and optical flow streams achiev
Externí odkaz:
http://arxiv.org/abs/2411.18002
Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities, particularly under real-world conditions where data often appear hea
Externí odkaz:
http://arxiv.org/abs/2411.17995
Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and
Externí odkaz:
http://arxiv.org/abs/2411.16173
Autor:
Yao, Man, Qiu, Xuerui, Hu, Tianxiang, Hu, Jiakui, Chou, Yuhong, Tian, Keyu, Liao, Jianxing, Leng, Luziwei, Xu, Bo, Li, Guoqi
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major challenges in realizing this vision: the performance gap between SNNs a
Externí odkaz:
http://arxiv.org/abs/2411.16061
The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage requirements while maintaining accuracy. However, this shifts the energy and
Externí odkaz:
http://arxiv.org/abs/2411.15982