With the rapid growth of social media and e-commerce, users are generating large amounts of textual review data on these platforms. Analyzing and mining these comment data is important for governments, enterprises, an...
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Sequence-to-sequence models provide a feasible new approach for generative text summarization, but these models are not able to accurately reproduce factual details and subject information. To address the problem of u...
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Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the...
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As one of the extremely important components on the transmission tower, the insulator has two functions of electrical insulation and wire fixing, which directly affects the operation of the power system. Defects in in...
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Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. ...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD 2 L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD 2 L surpasses existing state-of-the-art methods.
A B S T R A C TTo solve the visual semantic understanding bias and multimodal semantic bias in multimodal named entity recognition, the Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition (...
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The traditional particle swarm optimization (PSO) algorithm still has shortcomings in terms of performance and efficiency of cloud computing task scheduling, such as low local search efficiency and limited search accu...
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The traditional particle swarm optimization (PSO) algorithm still has shortcomings in terms of performance and efficiency of cloud computing task scheduling, such as low local search efficiency and limited search accuracy, which often makes it difficult to find the global optimal solution and easily falls into the local optimal solution. To solve this problem, an improved particle swarm optimization task scheduling algorithm(IPSO) was proposed. Firstly, a opposition-based learning strategy was used to create a more homogeneous initial population and the Rate of convergence of this algorithm was enhanced. Secondly, in the particle update process, the sine cosine algorithm(SCA) was introduced to enhance the optimization ability of the particles and balance the two processes of global search and local development. Finally, a search behavior based on average fitness was added to further expand the search solution space to find better optimal solutions and prevent falling into local optima. Experimental verification was conducted on the CloudSim simulation platform. The experimental results show that the improved particle swarm algorithm has significant advantages in reducing the cost and maximum completion time of system tasks. In particular, when the number of tasks reaches 500, IPSO improves the total cost by 10%, 4. 6%, 8. 6%, 9. 2%, 8. 2%, 10. 4% and 11. 3% respectively compared with adaptive particle swarm optimization (AdPSO), sine cosine algorithm-particle swarm optimization (SCA-PSO), simulated annealing particle swarm optimization (SAPSO), enhanced phagocytosis genetic algorithm (EPGA), competitive crossover mechanism genetic algorithm (C2PGA), opposition based learning-particle swarm optimization (OBL-PSO) and PSO, and improves the maximum completion time by 34. 1%, 27%, 41. 7%, 28. 5%, 21. 6%, 50. 3% and 54. 8% respectively, which verifies the feasibility and effectiveness of IPSO in solving cloud computing task scheduling problems under different task scales
In recent years, a series of methods have been proposed to use image semantics to assist in extracting named entities. However, in these multi-modal named entity recognition methods, there are problems of visual seman...
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Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous r...
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In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually e...
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