论文标题
政治海报标识和外观融合
Political Posters Identification with Appearance-Text Fusion
论文作者
论文摘要
在本文中，我们提出了一种有效利用外观特征和文本向量来准确对其他类似政治图像进行准确分类的方法。这项工作的大多数都集中在旨在促进某个政治事件的政治海报上，其自动化标识可以导致产生详细的统计数据，并满足各个领域的判断需求。从政治家和政治事件的全面关键字列表开始，我们首次策划了一个有效，实用的政治海报数据集，其中包含13K人体标记的政治形象，包括3K政治海报，这些海报明确支持运动或运动。其次，我们对该数据集进行了详尽的案例研究，并分析了政治海报的共同模式和异常值。最后，我们提出了一个模型，将外观和文本信息的力量结合在一起，以将政治海报的准确性显着。
In this paper, we propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters from other similar political images. The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event, and the automated identification of which can lead to the generation of detailed statistics and meets the judgment needs in a variety of areas. Starting with a comprehensive keyword list for politicians and political events, we curate for the first time an effective and practical political poster dataset containing 13K human-labeled political images, including 3K political posters that explicitly support a movement or a campaign. Second, we make a thorough case study for this dataset and analyze common patterns and outliers of political posters. Finally, we propose a model that combines the power of both appearance and text information to classify political posters with significantly high accuracy.
