Strong and weak stress were elicited by real-scene thesis defense and pre-defending presentation of the thesis work, and tri-axial accelerometer data were acquired and analyzed for their ability to recognize strong an...
详细信息
Strong and weak stress were elicited by real-scene thesis defense and pre-defending presentation of the thesis work, and tri-axial accelerometer data were acquired and analyzed for their ability to recognize strong and weak stress. Twenty-six subjects(7 females and 19 males) participated in the data acquisition experiment. A support vector machine classifier obtained a correct rate of 92.31% in the binary classification of the 26 subjects' tri-axial accelerometer data acquired during strong stress and weak stress.
Feature Pyramid Network (FPN) is one of the most popular feature fusion methods to address the multi-scale issue in object detection. Current FPN-based methods are mostly designed manually, which cannot guarantee the ...
详细信息
ISBN:
(数字)9781728113319
ISBN:
(纸本)9781728113326
Feature Pyramid Network (FPN) is one of the most popular feature fusion methods to address the multi-scale issue in object detection. Current FPN-based methods are mostly designed manually, which cannot guarantee the optimal feature fusion. Besides, the predetermined methods generally provide the same strategy to various targets, which are not distinctive among targets with different scales. In this paper, we present a novel dynamic feature fusion method based on the graph convolution network (GCN), called DG-FPN. The proposed GCN-based method can dynamically transfer knowledge with learnable weights across all nodes, making it possible to learn the optimal feature fusion for detectors. Furthermore, the pixel-based adjacency matrix is proposed to offer customized fusion strategy for each target, achieving dynamic feature fusion. To optimize matrix-driven learning, semantic information is introduced to guide the process of fusion. Experiments show that DG-FPN significantly improves the performance of baseline networks on the challenging MS-COCO object benchmark, especially in small objects.
Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional s...
详细信息
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to be brief and vague, thereby signific...
详细信息
Designing shared neural architecture plays an important role in multi-task learning. The challenge is that finding an optimal sharing scheme relies heavily on the expert knowledge and is not scalable to a large number...
详细信息
A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Fi...
详细信息
In computed tomography imaging, the 2×2 acquisition mode improves the projection collection efficiency and reduces the X-ray exposure time;however, the collected projection is low-resolution and the reconstructed...
详细信息
In computed tomography imaging, the 2×2 acquisition mode improves the projection collection efficiency and reduces the X-ray exposure time;however, the collected projection is low-resolution and the reconstructed image quality is poor. Although the super-resolution(SR) method can improve the quality of the acquired projection in 2×2 acquisition mode, the signal-to-noise ratio of the reconstructed image is still affected by the estimation errors between the SR sinograms and the high-resolution sinograms. In this study, a joint regularizedbased reconstruction method was proposed. Under the condition of obtaining SR sinograms, we utilized the system matrix in 1×1 and 2×2 projection acquisition modes to construct the fidelity terms. In addition, the block matching and total variation regularizations were used to fully depict the sparsity of images. The proposed reconstruction model was solved by the iterative alternating minimization method. The experimental results on real anthropomorphic phantom data show that the proposed method is capable of suppressing noises while maintaining image details, which are not observed in the reconstructed results of other compared methods.
Generating high-quality images that conform to the semantics of captions has numerous potential applications. However, text-to-image generation is a challenging task due to its cross-modality nature. Current generativ...
Generating high-quality images that conform to the semantics of captions has numerous potential applications. However, text-to-image generation is a challenging task due to its cross-modality nature. Current generative models are typically unstable, meaning that complex sentences can result in poor image quality. In this paper, we propose a novel model to bridge the domain gap arising from sentence complexity to achieve stable text-to-image generation. Our model includes two key modules, the attribute extraction module and the attribute fusion module. These modules can extract attributes from the captions and fuse them with image features to encourage the model to accurately understand the semantics. Our modules are plug-and-play and extensive experiments demonstrate that our approach outperforms the state-of-the-art GAN model. Our code and trained model are available at https://***/tantian21/stable-t2i-generation.
An AAR (algebraic attack resistant) Boolean function is considered having good capability against both classical and fast algebraic attacks. However, AAR is too hard to achieve. This paper studies the protection again...
详细信息
An AAR (algebraic attack resistant) Boolean function is considered having good capability against both classical and fast algebraic attacks. However, AAR is too hard to achieve. This paper studies the protection against fast algebraic attacks on rotation symmetric Boolean functions by discussing their fast algebraic immunity. The result shows that all the even n-variable rotation symmetric Boolean functions of degree (n-1) are not AAR because their fast algebraic immunity is at most (n-1). Furthermore, we find that some of the even n-variable rotation symmetric Boolean functions of degree n or (n-2) have fast algebraic immunity at most (n-1), too.
A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and out...
详细信息
暂无评论