As an emerging industry in China, cross-border e-commerce has enjoyed the leap-forward development, along with increasingly prominent problems. This paper aims at finding out the problems and related roots that hinder...As an emerging industry in China, cross-border e-commerce has enjoyed the leap-forward development, along with increasingly prominent problems. This paper aims at finding out the problems and related roots that hinder its development, through conducting an analysis on the export development of cross-border e-commerce, in order to look for solutions and countermeasures in favor of its sound development and to promote the liberalization development of foreign trade in China.展开更多
Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges...Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges associated with dancedriven multi-instrument music(MIDI)generation are twofold:(i)lack of a publicly available multi-instrument MIDI and video paired dataset and(ii)the weak correlation between music and video.To tackle these challenges,we have built the first multi-instrument MIDI and dance paired dataset(D2MIDI).Based on this dataset,we introduce a multi-instrument MIDI generation framework(Dance2MIDI)conditioned on dance video.Firstly,to capture the relationship between dance and music,we employ a graph convolutional network to encode the dance motion.This allows us to extract features related to dance movement and dance style.Secondly,to generate a harmonious rhythm,we utilize a transformer model to decode the drum track sequence,leveraging a cross-attention mechanism.Thirdly,we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task.A BERTlike model is employed to comprehend the context of the entire music piece through self-supervised learning.We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.展开更多
基于深度监督的学习结构应用于跨模态图文检索领域,弥补了不同数据模式之间的异质性差异,通过端到端的方式同时保持语义鉴别和模态不变性,有效地学习异构数据的共同表示。本文构建了图像和文本双模态CNN神经网络模型,对损失函数进行改进...基于深度监督的学习结构应用于跨模态图文检索领域,弥补了不同数据模式之间的异质性差异,通过端到端的方式同时保持语义鉴别和模态不变性,有效地学习异构数据的共同表示。本文构建了图像和文本双模态CNN神经网络模型,对损失函数进行改进,优化神经网络模型训练学习过程,以监督网络学习跨模态转换函数。在Pascal sentence数据集的基础上,增加了5种不同类别的图文内容,通过训练数据集调整神经网络模型参数,保存最优模型。实验结果表明,改进算法的图文匹配正确率最高达到了98.2%,通过改进损失函数将算法的平均精度值MAP(Mean average precision)提升到了0.716,较传统深度学习ACMR算法的MAP提高了6.2%,证明本文改进的算法有效提高了跨模态图文检索匹配的精度。展开更多
文摘As an emerging industry in China, cross-border e-commerce has enjoyed the leap-forward development, along with increasingly prominent problems. This paper aims at finding out the problems and related roots that hinder its development, through conducting an analysis on the export development of cross-border e-commerce, in order to look for solutions and countermeasures in favor of its sound development and to promote the liberalization development of foreign trade in China.
基金supported by the National Social Science Foundation Art Project(No.20BC040)China Scholarship Council(CSC)Grant(No.202306320525).
文摘Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges associated with dancedriven multi-instrument music(MIDI)generation are twofold:(i)lack of a publicly available multi-instrument MIDI and video paired dataset and(ii)the weak correlation between music and video.To tackle these challenges,we have built the first multi-instrument MIDI and dance paired dataset(D2MIDI).Based on this dataset,we introduce a multi-instrument MIDI generation framework(Dance2MIDI)conditioned on dance video.Firstly,to capture the relationship between dance and music,we employ a graph convolutional network to encode the dance motion.This allows us to extract features related to dance movement and dance style.Secondly,to generate a harmonious rhythm,we utilize a transformer model to decode the drum track sequence,leveraging a cross-attention mechanism.Thirdly,we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task.A BERTlike model is employed to comprehend the context of the entire music piece through self-supervised learning.We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.
文摘基于深度监督的学习结构应用于跨模态图文检索领域,弥补了不同数据模式之间的异质性差异,通过端到端的方式同时保持语义鉴别和模态不变性,有效地学习异构数据的共同表示。本文构建了图像和文本双模态CNN神经网络模型,对损失函数进行改进,优化神经网络模型训练学习过程,以监督网络学习跨模态转换函数。在Pascal sentence数据集的基础上,增加了5种不同类别的图文内容,通过训练数据集调整神经网络模型参数,保存最优模型。实验结果表明,改进算法的图文匹配正确率最高达到了98.2%,通过改进损失函数将算法的平均精度值MAP(Mean average precision)提升到了0.716,较传统深度学习ACMR算法的MAP提高了6.2%,证明本文改进的算法有效提高了跨模态图文检索匹配的精度。
