近年来社会拥挤逐渐成为营销领域的一个研究热点,但仍较为零散,大量研究着重于探寻引起社会拥挤的原因等。消费行为往往受到微妙的环境因素的影响,随着拥挤状况在消费场所中日益频现,人们的消费倾向会发生改变,尤其在不同情境下,是否会...近年来社会拥挤逐渐成为营销领域的一个研究热点,但仍较为零散,大量研究着重于探寻引起社会拥挤的原因等。消费行为往往受到微妙的环境因素的影响,随着拥挤状况在消费场所中日益频现,人们的消费倾向会发生改变,尤其在不同情境下,是否会对消费群体的特殊消费行为产生影响尚未进行研究。鉴于此,本文针对社会拥挤,借助pajek针对web of science上核心论文进行网络分析,在现有研究成果的可视化基础,构建以食品与医疗行业为整体的研究框架,进行社会拥挤对放纵性消费的双情境实验对比研究。结果表明,餐饮情境下社会拥挤会显著降低放纵性消费水平,而在医疗情境下社会拥挤则会显著增加放纵性消费的水平,以期为学术研究和企业的营销管理提供参考和建议。展开更多
With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Alth...With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent. Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently. In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution’s wider coverage in the spatial dimension. The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.展开更多
文摘近年来社会拥挤逐渐成为营销领域的一个研究热点,但仍较为零散,大量研究着重于探寻引起社会拥挤的原因等。消费行为往往受到微妙的环境因素的影响,随着拥挤状况在消费场所中日益频现,人们的消费倾向会发生改变,尤其在不同情境下,是否会对消费群体的特殊消费行为产生影响尚未进行研究。鉴于此,本文针对社会拥挤,借助pajek针对web of science上核心论文进行网络分析,在现有研究成果的可视化基础,构建以食品与医疗行业为整体的研究框架,进行社会拥挤对放纵性消费的双情境实验对比研究。结果表明,餐饮情境下社会拥挤会显著降低放纵性消费水平,而在医疗情境下社会拥挤则会显著增加放纵性消费的水平,以期为学术研究和企业的营销管理提供参考和建议。
文摘With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed. Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting). Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent. Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently. In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution’s wider coverage in the spatial dimension. The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.
