Multi-view clustering is a complex and significant task in the fields of machine learning and data mining. Most of the existing multi-view clustering models are for views with complete information. However, data loss inevitably occurs during data collection and transmission, leading to the problems of partial individual unalignment (IU) and individual missing (IM). To address these challenges, the article proposes a framework called incomplete multi-view clustering with multiple contrastive learning and attention mechanism (IMCLAM). IMCLAM utilizes the maximization of mutual information of different views and enhance the separability of the representation through multiple contrastive learning and the fusion of specific low-dimensional representations into a joint representation through an attentional fusion layer. Moreover, the effect of negative samples is reduced by increasing the noise robustness loss. Experiments on four multi-view datasets demonstrate the effectiveness of IMCLAM on the task of multi-view clustering compared to six state-of the-art methods.
Water surface object detection is one of the essential techniques for carrying out water-related tasks and assisting in the conduct of vessels. However, water surface object detection algorithms currently have low-precision problems detecting small and irregular objects, and difficult samples are hard to detect in harsh environments. To this end, an improved loss and enhancement feature fusion method is proposed to optimize the YOLOX object detection algorithm for water surface target detection. YOLOX, as an anchorless frame algorithm, has better detection capability for irregular objects. Improved loss improves the focus on difficult samples and treats positive samples asymmetrically based on EIoU loss. The Lite-RFP enhanced feature fusion mechanism enables the network to recursively pass contextual information recursively. It enables the shallow network to integrate deep semantic information better, improving the performance of small target detection and maintaining a lighter network structure. The experimental results show that the improved algorithm based on YOLOXs improves the mAP value by 4.11 percentage points compared to the original YOLOX-l. At the same time, a real-time detection speed of 56.534 FPS can be achieved, and the detection problems caused by different water surface environments are improved.
In recent years, the application of medical image semantic segmentation tasks in medical diagnosis and treatment planning has received widespread attention from the research community. The High-Resolution Network (HRNet) has good adaptability to high-resolution and high-scale medical images. In this paper, a novel high-resolution serial feature fusion encoding and decoding structure is proposed, and a CBAM attention mechanism is fused to construct a module that can jointly focus on spatial, channel, and multi-scale hierarchical information, which can improve the feature representation ability of the model and effectively reduce parameter complexity. We use the HRNet architecture to construct our model. Experimental results show that our method achieves MIoU coefficient of 98.44% on the Kvasir-SEG dataset, which is 1.43 percentage points higher than the original HR-Net model, validating the effectiveness and reliability of our method.
In Chinese sentiment analysis, sentiment words are just a drop in the ocean compared with the whole corpus. In order to solve the problem of insufficient emotion lexicon and prior knowledge, proposes a method to predict the emotion intensity of target words based on neural network model (Neural Network Emebdding Score, NNES). By training a small number of labeled samples, using clustering algorithm to find the seed words, calculate the similarity between the target words and the seed words, and using it as the input of neural network to predict the emotional intensity of the unlabeled words. Compared with the traditional machine learning regression models, it has smaller mean square error. Meanwhile, a BiGRU model based on attention mechanism and convolution is proposed by integrating the predicted emotion intensity with word vector (Neural Network Emebdding Score with CNN and Attention-BiGRU, NNESC-Att-BiGRU). To compare several popular models on product and hotel review data sets, and the proposed model has better classification effect on Chinese sentiment classification task.
Colon cancer is one of the most common malignant tumors in human digestive tract, with high fatality and morbidity. Therefore, early screening of intestinal polyps to judge the probability of colon cancer has very important medical significance for the prevention and treatment of modern colon cancer. In this paper, a polyp segmentation algorithm combining multi-scale attention and multi-layer loss is proposed for the segmentation of intestinal polyp lesions during polyp screening. This algorithm is based on the improved U-NET network. On this basis, it combines DenseNet and adds multi-scale input based on multi-linear interpolation, coordinate attention mechanism and Dice loss to the network to jointly improve the joint segmentation performance of the model. The experimental results of the proposed algorithm on an open data set are better than those of U-NET and other algorithms, and the experimental results show that the proposed algorithm can effectively segment the lesion region of intestinal polyps with good segmentation performance.
Compensating the illumination of a face image is an important process to achieve effective face recognition under severe illumination conditions. This paper present a novel illumination normalization method which specifically considers removing the illumination boundaries as well as reducing the regional illumination. We begin with the analysis of the commonly used reflectance model and then expatiate the hybrid usage of adaptive non-local smoothing and the local information coding based on Weber’s law. The effectiveness and advantages of this combination are evidenced visually and experimentally. Results on Extended YaleB database show its better performance than several other famous methods.
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