Design and implementation of virtual fitting system based on gesture recognition and clothing transfer algorithm

Wu, Ying; Liu, Hongbing; Lu, Pengzhen; Zhang, Lihua; Yuan, Fangjian

doi:10.1038/s41598-022-21734-y

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Poly-GAN presents an advancement in fashion synthesis compared to other models [176], as it operates with multiple conditional inputs and achieves satisfactory fitting results without requiring 3D model information [177]. However, the generated images can exhibit texture deformation and body part loss, affecting the fitting outcomes [178]. Further research is needed to address these issues in this domain.…”

Section: Single-image Gan (Singan))mentioning

confidence: 99%

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Chakraborty,

Reddy K S,

Naik

et al. 2024

Mach. Learn.: Sci. Technol.

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Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas, since their inception in 2014. Consisting of a discriminative network and a generative network engaged in a Minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ``Top Ten Global Breakthrough Technologies List'' issued by the Massachusetts Science and Technology Review. Over the years, numerous advancements have been proposed, leading to a rich array of GAN variants, such as conditional GAN, Wasserstein GAN, CycleGAN, and StyleGAN, among many others. This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants. We also delve into recent theoretical developments, exploring the profound connection between the adversarial principle underlying GAN and Jensen-Shannon divergence while discussing the optimality characteristics of the GAN framework. The efficiency of GAN variants and their model architectures will be evaluated along with training obstacles as well as training solutions. In addition, a detailed discussion will be provided, examining the integration of GANs with newly developed deep learning frameworks such as Transformers, Physics-Informed Neural Networks, Large Language models, and Diffusion models. Finally, we reveal several issues as well as future research outlines in this field.

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Section: Single-image Gan (Singan))mentioning

confidence: 99%

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Chakraborty,

Reddy K S,

Naik

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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Design and Simulation of Heading Controller for Unmanned Boat Based on Fuzzy Neural PID

Chen

Pan³

et al. 2023

Communications in Computer and Information Science

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Hierarchical graph contrastive learning of local and global presentation for multimodal sentiment analysis

Du,

Jin,

Zhuang

et al. 2024

Sci Rep

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Multi-modal sentiment analysis (MSA) aims to regress or classify the overall sentiment of utterances through acoustic, visual, and textual cues. However, most of the existing efforts have focused on developing the expressive ability of neural networks to learn the representation of multi-modal information within a single utterance, without considering the global co-occurrence characteristics of the dataset. To alleviate the above issue, in this paper, we propose a novel hierarchical graph contrastive learning framework for MSA, aiming to explore the local and global representations of a single utterance for multimodal sentiment extraction and the intricate relations between them. Specifically, regarding to each modality, we extract the discrete embedding representation of each modality, which includes the global co-occurrence features of each modality. Based on it, for each utterance, we build two graphs: local level graph and global level graph to account for the level-specific sentiment implications. Then, two graph contrastive learning strategies is adopted to explore the different potential presentations based on graph augmentations respectively. Furthermore, we design a cross-level comparative learning for learning local and global potential representations of complex relationships.

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Design and implementation of virtual fitting system based on gesture recognition and clothing transfer algorithm

Cited by 5 publications

References 29 publications

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

Design and Simulation of Heading Controller for Unmanned Boat Based on Fuzzy Neural PID

Hierarchical graph contrastive learning of local and global presentation for multimodal sentiment analysis

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