Software-defined Networking (SDN) is an innovative network architecture tailored to address the modern demands of network virtualization and cloud computing, which require features such as programmability, flexibility...
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Online shopping has become an integral part of modern consumer culture. Yet, it is plagued by challenges in visualizing clothing items based on textual descriptions and estimating their fit on individual body types. I...
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Online shopping has become an integral part of modern consumer culture. Yet, it is plagued by challenges in visualizing clothing items based on textual descriptions and estimating their fit on individual body types. In this work, we present an innovative solution to address these challenges through text-driven clothed human image synthesis with 3D human model estimation, leveraging the power of Vector Quantized Variational AutoEncoder (VQ-VAE). Creating diverse and high-quality human images is a crucial yet difficult undertaking in vision and graphics. With the wide variety of clothing designs and textures, existing generative models are often not sufficient for the end user. In this proposed work, we introduce a solution that is provided by various datasets passed through several models so the optimized solution can be provided along with high-quality images with a range of postures. We use two distinct procedures to create full-body 2D human photographs starting from a predetermined human posture. 1) The provided human pose is first converted to a human parsing map with some sentences that describe the shapes of clothing. 2) The model developed is then given further information about the textures of clothing as an input to produce the final human image. The model is split into two different sections the first one being a codebook at a coarse level that deals with overall results and a fine-level codebook that deals with minute detailing. As mentioned previously at fine level concentrates on the minutiae of textures, whereas the codebook at the coarse level covers the depictions of textures in structures. The decoder trained together with hierarchical codebooks converts the anticipated indices at various levels to human images. The created image can be dependent on the fine-grained text input thanks to the utilization of a blend of experts. The quality of clothing textures is refined by the forecast for finer-level indexes. Implementing these strategies can result
As the applications of large language models (LLMs) expand across diverse fields, their ability to adapt to ongoing changes in data, tasks, and user preferences becomes crucial. Traditional training methods with stati...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loa...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network *** To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is *** K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search *** proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the *** The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering *** calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network *** This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network *** research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
Leukemia is a cancer which is characterized due to immature lymphocytes in the blood samples. Effectual diagnosis of leukemia is majorly linked with earlier treatment of the disease. The present practice of detecting ...
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The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
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Contrastive quantization (applying vector quantization to contrastive learning) has achieved great success in large-scale image retrieval because of its advantage of high computational efficiency and small storage spa...
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The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates th...
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Image captioning is a multimodal task that involves both computer vision and natural language processing. In recent years, to address the issue of insufficient visual information, multiple features are often used. How...
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As gravitational wave astronomy has advanced, the need for effective and quick signal processing has never been more critical. New detectors such as Laser Interferometer Gravitational-Wave Observatory (LIGO) produces ...
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