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Multi-intent Aware Contrastive Learning for Sequential Recommendation

Autor: Huang, Junshu, Long, Zi, Fu, Xianghua, Chen, Yin

Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on single-intent representations to direct the training process. However,

Externí odkaz: http://arxiv.org/abs/2409.08733

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Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets

Autor: Long, Zi, Tang, Zhenhao, Fu, Xianghua, Chen, Jian, Hou, Shilong, Lyu, Jinze

Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages. However, most of these conclusions are drawn from the analysis of experimental resul

Externí odkaz: http://arxiv.org/abs/2404.06107

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Direct measurement of acoustic spectral density and fractional topological charge

Autor: Ge, Hao, Long, Zi-Wei, Xu, Xiang-Yuan, Liu, Yang, Xie, Bi-Ye, Jiang, Jian-Hua, Lu, Ming-Hui, Chen, Yan-Feng

Local density-of-states (LDOS) is a fundamental spectral property that plays a central role in various physical phenomena such as wave-matter interactions. Here, we report on the direct measurement of the LDOS of acoustic systems and derive from whic

Externí odkaz: http://arxiv.org/abs/2208.11882

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Multimodal Neural Machine Translation with Search Engine Based Image Retrieval

Autor: Tang, ZhenHao, Zhang, XiaoBing, Long, Zi, Fu, XiangHua

Recently, numbers of works shows that the performance of neural machine translation (NMT) can be improved to a certain extent with using visual information. However, most of these conclusions are drawn from the analysis of experimental results based

Externí odkaz: http://arxiv.org/abs/2208.00767

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