Multi-attention based cross-domain beauty product image retrieval

Wang, Zhihui; Xing, Ling; Jia-jun, Lin; Yang, Caifei; Li, Haojie

doi:10.1007/s11432-019-2721-0

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Next, we assess the contributions of each component in our method to verify their effectiveness. Finally, we compare our approach with state-of-the-art methods, conducting evaluations on three benchmark datasets (CUB200-2011, 24 Perfect500k, 25 and Stanford Online Products 26 ) to demonstrate the superiority of our method.…”

Section: Resultsmentioning

confidence: 99%

Length and salient losses co-supported content-based commodity retrieval neural network

Chen,

Wang,

Sun

et al. 2024

J. Electron. Imag.

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Section: Resultsmentioning

confidence: 99%

Length and salient losses co-supported content-based commodity retrieval neural network

Chen,

Wang,

Sun

et al. 2024

J. Electron. Imag.

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“…It is worth noting that the text reconstructed by the proposed method allows capturing key information of text descriptions, compensating for the interference caused by redundant or irrelevant information and noise. In the future, we plan to apply the attention mechanism [36] to improve the accuracy of the text-reconstruction network in the key information extraction [37].…”

Section: Resultsmentioning

confidence: 99%

Triple discriminator generative adversarial network for zero-shot image classification

Yan

Wang

et al. 2021

Sci. China Inf. Sci.

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One key challenge in zero-shot classification (ZSC) is the exploration of knowledge hidden in unseen classes. Generative methods such as generative adversarial networks (GANs) are typically employed to generate the visual information of unseen classes. However, the majority of these methods exploit global semantic features while neglecting the discriminative differences of local semantic features when synthesizing images, which may lead to sub-optimal results. In fact, local semantic information can provide more discriminative knowledge than global information can. To this end, this paper presents a new triple discriminator GAN for ZSC called TDGAN, which incorporates a text-reconstruction network into a dual discriminator GAN (D2GAN), allowing to realize cross-modal mapping from text descriptions to their visual representations. The text-reconstruction network focuses on key text descriptions for aligning semantic relationships to enable synthetic visual features to effectively represent images. Sharma-Mittal entropy is exploited in the loss function to make the distribution of synthetic classes be as close as possible to the distribution of real classes. The results of extensive experiments over the Caltech-UCSD Birds-2011 and North America Birds datasets demonstrate that the proposed TDGAN method consistently yields competitive performance compared to several state-of-the-art ZSC methods.

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“…This section describes the multi-attention mechanism used in our framework, which has shown high capability in extracting the features and dependencies from modalities (Zhang et al 2022;Wang et al 2020;Song et al 2022).…”

Section: Modality Feature Extraction and Fusionmentioning

confidence: 99%

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Zhou

et al. 2023

AAAI

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Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.

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Multi-attention based cross-domain beauty product image retrieval

Cited by 7 publications

References 7 publications

Length and salient losses co-supported content-based commodity retrieval neural network

Length and salient losses co-supported content-based commodity retrieval neural network

Triple discriminator generative adversarial network for zero-shot image classification

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

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