International Journal of Computer Vision - Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently... 相似文献
High-level semantic features and low-level detail features matter for salient object detection in fully convolutional neural networks (FCNs). Further integration of low-level and high-level features increases the ability to map salient object features. In addition, different channels in the same feature are not of equal importance to saliency detection. In this paper, we propose a residual attention learning strategy and a multistage refinement mechanism to gradually refine the coarse prediction in a scale-by-scale manner. First, a global information complementary (GIC) module is designed by integrating low-level detailed features and high-level semantic features. Second, to extract multiscale features of the same layer, a multiscale parallel convolutional (MPC) module is employed. Afterwards, we present a residual attention mechanism module (RAM) to receive the feature maps of adjacent stages, which are from the hybrid feature cascaded aggregation (HFCA) module. The HFCA aims to enhance feature maps, which reduce the loss of spatial details and the impact of varying the shape, scale and position of the object. Finally, we adopt multiscale cross-entropy loss to guide network learning salient features. Experimental results on six benchmark datasets demonstrate that the proposed method significantly outperforms 15 state-of-the-art methods under various evaluation metrics.
As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.
Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other... 相似文献
Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this... 相似文献
Due to air turbulence, large areas of coal will fall when the special coal-transportation trains pass the tunnel exits and entrances. Aiming at the problems of low efficiency and high cost of manual cleaning for long distance coal cleaning in the tunnel, a new railway tunnel fallen coal dust collection device which was composed of a main conveying coal feeding pipe and multiple branch pipes of coal suction was designed. It was used to clean the small particles and lightweight railway tunnel fallen coal. Firstly, the gas-solid two-phase flow model based on the Euler-Lagrange approach for the design of the main conveying coal feeding pipe was established in the coal conveying pipelines. Secondly, the effect of the coal particles' incident angle and multiple branch pipe spacing on the main coal conveying pipe flow field, which was based on Fluent finite element simulation software, was studied. What was more, the optimal angle of incidence and the optimal value of the number of branch coal suction pipe, which was installed on the main conveying pipe, were analyzed. Finally, the finite element simulation was verified by field test. Simulation and experimental results showed that it was more conducive to the railway tunnel fallen coal transportation when coal particles' incident angle was less than 45° and the branch pipe spacing was in the vicinity of 750 mm. For that when incident angle was less than 45°, the main conveying coal pipe pressure-drop became weaker and particle flow could obtain large horizontal transport velocity. And when the branch pipe spacing was in the vicinity of 750 mm, the horizontal transport velocity had a smaller fluctuation range and the transportation of coal was larger than that of the other groups. The research results are of great significance to improve the structure of the main conveying coal pipe, increase the efficiency of tunnel coal conveying and optimize the railway tunnel coal dust collection device. 相似文献
