However, the predictive feature maps of FPNs also lose a portion of the location information while enhancing the semantic information and are limited to unidirectional information flow. For this reason, the Path Aggregation Network (PANet) [33] constructs bottom–up pathways on top of FPNs to ...
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ns1.google.com Found: admin.google.com Found: www.google.com Found: m.google.com Found: support.google.com Found: translate.google.com Found: cse.google.com Found: news.google.com Found: music.google.com Found: mail.google.com Found: store.google.com Found: mobile.google.com Found: sea...
所以本文希望生成模型能够识别这种情况, 因为文本建模的主要目的就是, 发现文档之间, 文档与查询之间的主题相关性。 3.3 有限混合模型 (Finite Mixture of unigram) 有限混合模型[7], 也称为聚类模型, 或者主题模型, 是上面的单元模型的一种自然演化, 为了处理异构 (heterogeneous) 的文档集合而做出了不同的假设...
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However, the predictive feature maps of FPNs also lose a portion of the location information while enhancing the semantic information and are limited to unidirectional information flow. For this reason, the Path Aggregation Network (PANet) [33] constructs bottom–up pathways on top of FPNs to ...