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pro vyhledávání: '"Du, Qian"'
Recently Transformer-based hyperspectral image (HSI) change detection methods have shown remarkable performance. Nevertheless, existing attention mechanisms in Transformers have limitations in local feature representation. To address this issue, we p
Externí odkaz:
http://arxiv.org/abs/2411.14109
Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution, and many tensor-based approaches to this task have been recently propose
Externí odkaz:
http://arxiv.org/abs/2409.18731
In multi-source remote sensing image classification field, remarkable progress has been made by convolutional neural network and Transformer. However, existing methods are still limited due to the inherent local reductive bias. Recently, Mamba-based
Externí odkaz:
http://arxiv.org/abs/2408.14255
Hyperspectral object tracking (HOT) has exhibited potential in various applications, particularly in scenes where objects are camouflaged. Existing trackers can effectively retrieve objects via band regrouping because of the bias in existing HOT data
Externí odkaz:
http://arxiv.org/abs/2408.12232
In this study, we uncover the intrinsic information processes in non-Hermitian quantum systems and their thermodynamic effects. We demonstrate that these systems can exhibit negative entropy production, making them potential candidates for informatio
Externí odkaz:
http://arxiv.org/abs/2408.04177
Autor:
Du, Qian, Mao, Yong-Hua
We will represent the so-called Perron-Frobenius eigenvector (if exists) for infinite non-negative matrix $A$ and Metzler matrix by using its corresponding Markov chain with probability transition function.
Externí odkaz:
http://arxiv.org/abs/2407.19964
Autor:
Du, Qian, Mao, Yong-Hua
For the continuous-time $\lambda$-recurrent jump process, the $\lambda$-recurrence assures the existence of quasi-stationary distribution when it has finite exit states (the states that have positive killing rates). And we give an explicit representa
Externí odkaz:
http://arxiv.org/abs/2407.19803
Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. However, simultaneously modeling global and local features is rarely explored to enhance HSI denoising. In this letter, we propose a
Externí odkaz:
http://arxiv.org/abs/2403.10067
Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods pri
Externí odkaz:
http://arxiv.org/abs/2403.05852
Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling. Meanwhile, existing MIM-
Externí odkaz:
http://arxiv.org/abs/2311.04442