Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these met...
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Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact...
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The evolution of advanced artificial intelligence generated content approaches has heightened concerns about deepfake, due to the sophisticated forgeries and concealed appearances they produce. To this end, the pre-tr...
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The evolution of advanced artificial intelligence generated content approaches has heightened concerns about deepfake, due to the sophisticated forgeries and concealed appearances they produce. To this end, the pre-trained Vision Transformer (ViT) model has become a de facto choice for deepfake detection, thanks to its powerful learning capability. Despite favorable results achieved by existing ViT-based methods, they have inherent limitations that could result in suboptimal performance in scenarios with continuously evolving forgery techniques, such as overfitting to single forgery patterns or placing excessive emphasis on dominant forgery regions. In this paper, we propose CUTA, a simple yet effective deepfake detection paradigm that utilizes ViT adapters as the medium and fully exploits the spatial- and frequency-domain features of given images to overcome the limitations of existing methods. Specifically, CUTA focuses on frequency domain masking within the input space, which obscures parts of the high-frequency image to intensify the training challenge while preserving subtle forgery cues in the frequency domain to facilitate comprehensive forgery representations. Furthermore, we propose two task-customized modules within the ViT model, i.e., the texture enhancement module and the multi-scale perceptron module, to seamlessly integrate local texture and rich contextual features. These two modules ensure an organic interaction between the task-specific forgery patterns and general semantic features within the pre-trained ViT framework. The experimental results on several publicly available benchmark datasets demonstrate CUTA’s superiority in performance, particularly showcasing its significant advantages in both cross-dataset and cross-manipulation scenarios. 2005-2012 IEEE.
Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit ...
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Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective me...
Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.
Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically foc...
Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.
The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Misinformation Detection (MD), aiming to...
This paper is concerned with the unique identification of the shape of a scatterer through a single far-field pattern in an inverse elastic medium scattering problem with a generalized transmission boundary condition....
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The training of generative adversarial networks (GANs) is usually vulnerable to mode collapse and vanishing gradients. The evolutionary generative adversarial network (E-GAN) attempts to alleviate these issues by opti...
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Multi-label feature selection attracts considerable attention from multi-label learning. Information-theory based multi-label feature selection methods intend to select the most informative features and reduce the unc...
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