Micro-expression recognition (MER) presents a significant challenge due to the transient and subtle nature of the motion changes involved. In recent years, deep learning methods based on attention mechanisms have made...
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Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to gener...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to generate corresponding HR-HSIs by processing low-resolution hyperspectral images (LR-HSIs). Compared to multi-source data fusion methods, SHSR relies solely on a single low-resolution image and does not require additional auxiliary information or multimodal data, making it more flexible and efficient in data acquisition. Recently, Kolmogorov–Arnold Networks (KAN), which derive from the Kolmogorov–Arnold representation theorem, show great potential in modeling long-range dependencies. In this paper, we further investigate the potential of KAN for hyperspectral image restoration. Specifically, we propose a spatial-spectral attention block (SSAB) module, which includes a KAN-based spatial attention module (KAN-SpaAB) and a KAN-based spectral attention module (KAN-SpeAB), designed for the restoration of spatial and spectral information, respectively. Experimental results demonstrate that KSSANet outperforms existing methods in both quantitative evaluation and image generation quality, achieving state-of-the-art (SOTA) performance. Our code is available at: https://***/Baisonm-Li/KSSANet.
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph...
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Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the hig...
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Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus...
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Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined...
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Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable *** algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.
In this paper,for a zero-dimensional polynomial ideal I,the authors prove that k[x_(1),x_(2),…,x_(n)]/I is cyclic if and only if the breadth of I is 0 or ***,the authors present a new algorithm to compute polynomial ...
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In this paper,for a zero-dimensional polynomial ideal I,the authors prove that k[x_(1),x_(2),…,x_(n)]/I is cyclic if and only if the breadth of I is 0 or ***,the authors present a new algorithm to compute polynomial univariate representation(PUR)of such an ideal.
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled ...
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Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computati...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.
Automatic cell/nucleus detection is a prerequisite for various quantitative analyses on microscopy image. However, previous deep learning methods require enough annotated microscopy images for better performance, whic...
Automatic cell/nucleus detection is a prerequisite for various quantitative analyses on microscopy image. However, previous deep learning methods require enough annotated microscopy images for better performance, which is a great challenge for microscopy image due to limited annotation and high cost. This paper proposes an end-to-end adversarial learning model with unsupervised domain adaptation for cell/nucleus detection. Different staining microscopy images transformation and cell/nucleus detection are merged into one end-to-end model to achieve mutual restriction of accuracy. Furthermore, a cross-domain consistency loss is introduced, which can refine the results of image transformation and localize cells synchronously. The experiments conclude that proposed method achieves the best F1 scores compared with other methods on cell/nucleus detection of different staining microscopy images. Moreover, ablation study also approves the effectiveness of cross-domain consistency loss.
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