Atmospheric duct (AD) is a natural phenomenon affected by humidity or air pressure diversities, which impacts the propagation of electromagnetic signals. Recent studies have been focused on ADH prediction based on met...
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The paper presents multimodal human-computer interaction using speech and gesture recognition to develop a system for mouse movement and operation. The approach allows users to perform mouse navigation and various mou...
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The philosophical literature that tackles foundational questions about normativity often appeals to normative reasons—or considerations that count in favor of or against actions—and their interaction. The interactio...
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This paper presents modern architectures for effective speech synthesis. Since each language has its own subtleties, the task of applying the world methods for the Uzbek language was relevant, due to the lack of resea...
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In StyleGAN, convolution kernels are shaped by both static parameters shared across images and dynamic modulation factors w(+) is an element of W+ specific to each image. Therefore, W+ space is often used for image in...
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ISBN:
(纸本)9789819786916;9789819786923
In StyleGAN, convolution kernels are shaped by both static parameters shared across images and dynamic modulation factors w(+) is an element of W+ specific to each image. Therefore, W+ space is often used for image inversion and editing. However, pre-trained model struggles with synthesizing out-of-domain images due to the limited capabilities of W+ and its resultant kernels, necessitating full fine-tuning or adaptation through a complex hypernetwork. This paper proposes an efficient refining strategy for dynamic kernels. The key idea is to modify kernels by low-rank residuals, learned from input image or domain guidance. These residuals are generated by matrix multiplication between two sets of tokens with the same number, which controls the complexity. We validate the refining scheme in image inversion and domain adaptation. In the former task, we design grouped transformer blocks to learn these token sets by one- or two-stage training. In the latter task, token sets are directly optimized to support synthesis in the target domain while preserving original content. Extensive experiments show that our method achieves low distortions for image inversion and high quality for out-of-domain editing.
Accurate polyp segmentation is crucial for the early detection of colorectal cancer. However, existing polyp detection methods sometimes ignore multi-directional features and the drastic scale changes of concealed tar...
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ISBN:
(纸本)9789819784950;9789819784967
Accurate polyp segmentation is crucial for the early detection of colorectal cancer. However, existing polyp detection methods sometimes ignore multi-directional features and the drastic scale changes of concealed targets. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming sets of orthogonal feature vector basis, which solves the issue of random feature direction changes. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention (ERA) module is used to re-combine effective features, and Shallow Reverse Attention (SRA) mechanism is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.
Self-supervised low-dose Computed Tomography (LDCT) imaging methods have demonstrated significant clinical potential as they can train an efficient denoising model without high-quality normal-dose CT (NDCT) images. Ho...
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ISBN:
(纸本)9789819784950;9789819784967
Self-supervised low-dose Computed Tomography (LDCT) imaging methods have demonstrated significant clinical potential as they can train an efficient denoising model without high-quality normal-dose CT (NDCT) images. However, existing methods only focus on improving overall quality of the images, potentially resulting in the loss of details in critical areas when subjected to high levels of noise. To address this issue, we develop a mask guided network to enhance the quality of the desired regions in a self-supervised manner. Firstly, an adaptive organ segmentation model is trained with efficient fine-tuning strategies based on the Segment Anything Model. Secondly, we utilize the proposed segmentation model to generate mask embeddings for each LDCT image, incorporating both positional information of the target (e.g., liver and kidney) and latent image features. Finally, we propose a novel noise reduction network that incorporates mask embedding into self-supervised learning to recover high-quality CT images. Comprehensive comparisons and analyses on two datasets have demonstrated that the proposed method can achieve excellent performance in suppressing overall noise and improve imaging quality in key areas.
Understanding the fault tolerance of Byzantine Agreement protocols is an important question in distributed computing. While the setting of Byzantine faults has been thoroughly explored in the literature, the (arguably...
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Recently, the rapid development of deepfake technology attracted strong attention from the community. Some previous work on deepfake detection achieved good results in the frequency domain, which inspires us to combin...
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For k≥ 2, let H(V, E) be a k-uniform connected hypergraph with maximum degree Δ on n vertices and m edges. A set of edges A⊆ E is a matching if every two distinct edges in A have no common vertices. The matchin...
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