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 Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability. Owing to these properties, the Swin Transformer is suitable for e...
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Introduction: Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream ***: The paper aims to recover the underlying surface of noisy point ***: We desig...
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Video saliency prediction is an important task in the field of computer vision. Most of the existing video saliency prediction methods only focus on image information, and the audio information is often ignored. This ...
Video saliency prediction is an important task in the field of computer vision. Most of the existing video saliency prediction methods only focus on image information, and the audio information is often ignored. This leads to an incomplete perception mode, which makes it difficult to achieve optimal performance. SENet is an excellent attention mechanism-based network. It significantly enhances the performance of 2D convolutional networks. However, whether the 3D convolutional network can be applied to this attention mechanism network remains to be studied. In order to solve the above problems, we propose a saliency prediction network for audio-visual fusion to extract and predict various information in videos. At the same time, we improve the traditional SENet to make it applicable in 3D convolutional neural networks and discuss its role. Compared with the state-of-the-art methods, our model has strong competitiveness in multiple data sets.
This paper unveils and investigates a novel quasi-Minnaert resonance for an elastic hard inclusion embedded in a soft homogeneous medium in the sub-wavelength regime. The quasi-Minnaert resonance consists of boundary ...
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3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spect...
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Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model counting, approximate techniques hav...
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' pr...
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In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this techniq...
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In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof ...
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